Image-Text-to-Text
Transformers
Safetensors
glm5_next
glm
exl3
tr3
vllm
sm120
nvfp4
dflash2
multimodal
shapleymcg
conversational
Eval Results (legacy)
4-bit precision
Instructions to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="brandonmusic/GLM-5.3-Flash-tr3-4bpw") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("brandonmusic/GLM-5.3-Flash-tr3-4bpw") model = AutoModelForMultimodalLM.from_pretrained("brandonmusic/GLM-5.3-Flash-tr3-4bpw", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "brandonmusic/GLM-5.3-Flash-tr3-4bpw" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brandonmusic/GLM-5.3-Flash-tr3-4bpw", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/brandonmusic/GLM-5.3-Flash-tr3-4bpw
- SGLang
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "brandonmusic/GLM-5.3-Flash-tr3-4bpw" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brandonmusic/GLM-5.3-Flash-tr3-4bpw", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "brandonmusic/GLM-5.3-Flash-tr3-4bpw" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brandonmusic/GLM-5.3-Flash-tr3-4bpw", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use brandonmusic/GLM-5.3-Flash-tr3-4bpw with Docker Model Runner:
docker model run hf.co/brandonmusic/GLM-5.3-Flash-tr3-4bpw
Add files using upload-large-folder tool
Browse files- .gitattributes +1 -0
- .materialization/shards/model-00001-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00002-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00003-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00004-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00005-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00006-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00007-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00008-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00009-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00010-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00011-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00012-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00013-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00014-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00015-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00016-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00017-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00018-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00019-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00020-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00021-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00022-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00023-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00024-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00025-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00026-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00027-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00028-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00029-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00030-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00031-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00032-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00033-of-00120.safetensors.json +0 -0
- .materialization/shards/model-00034-of-00120.safetensors.json +0 -0
- LICENSE +21 -0
- MANIFEST.json +1 -0
- README.md +29 -0
- SHA256SUMS +328 -0
- chat_template.jinja +210 -0
- config.json +300 -0
- exl3-mcg-storage-abi.json +1 -0
- generation_config.json +12 -0
- materialization-receipt.json +1 -0
- processor_config.json +44 -0
- provenance/source-model-revision.json +1 -0
- quantization/recipe.json +1 -0
- receipts/checkpoint.json +1 -0
- runtime/src/quant_pipeline/scoring/blend.py +69 -0
- tokenizer_config.json +33 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
.materialization/shards/model-00001-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00002-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00003-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00004-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00005-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00006-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00007-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00008-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00009-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00010-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00011-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00012-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00013-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00014-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00015-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00016-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00017-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00018-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00019-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00020-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00021-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00022-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00023-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00024-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00025-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00026-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00027-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00028-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00029-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00030-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00031-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00032-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00033-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
.materialization/shards/model-00034-of-00120.safetensors.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
LICENSE
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
MIT License
|
| 2 |
+
|
| 3 |
+
Copyright (c) 2026 Z.AI Co., Ltd
|
| 4 |
+
|
| 5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 6 |
+
of this software and associated documentation files (the "Software"), to deal
|
| 7 |
+
in the Software without restriction, including without limitation the rights
|
| 8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 9 |
+
copies of the Software, and to permit persons to whom the Software is
|
| 10 |
+
furnished to do so, subject to the following conditions:
|
| 11 |
+
|
| 12 |
+
The above copyright notice and this permission notice shall be included in all
|
| 13 |
+
copies or substantial portions of the Software.
|
| 14 |
+
|
| 15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 21 |
+
SOFTWARE.
|
MANIFEST.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"file_count":328,"files":[{"bytes":1570,"path":".gitattributes","sha256":"34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930"},{"bytes":186336,"path":".materialization/shards/model-00001-of-00120.safetensors.json","sha256":"8d0185b871ad9cd46bfd2495a01675046b306a8510a62cea36aaa3e3cbf51f1f"},{"bytes":535867,"path":".materialization/shards/model-00002-of-00120.safetensors.json","sha256":"077b2416ef12b929a278b1b86935159ab35f88ac2306f5be7e36e5d07b901a76"},{"bytes":535027,"path":".materialization/shards/model-00003-of-00120.safetensors.json","sha256":"c2103a15e275cc17e3217299638a877bbc236839d258ee6837fb4fc491d3a88e"},{"bytes":510894,"path":".materialization/shards/model-00004-of-00120.safetensors.json","sha256":"73d7098787cb673ee5d898033f796e0e2ad4e80ec19ddc1bb4a7d7a89d7c8858"},{"bytes":535875,"path":".materialization/shards/model-00005-of-00120.safetensors.json","sha256":"8cee876d2e1e783efe86a3b130e3c3b8e7fd7df4e01e82586c454151da882c7c"},{"bytes":534435,"path":".materialization/shards/model-00006-of-00120.safetensors.json","sha256":"6b293a767b22b48b4d80472891b5e498cb8e89c32a9a4d029d38fc4652e86dbe"},{"bytes":513040,"path":".materialization/shards/model-00007-of-00120.safetensors.json","sha256":"47c858836e8ab1a6e495e150c0b0c63f315454c7a796e45d385c75e021b9d192"},{"bytes":535875,"path":".materialization/shards/model-00008-of-00120.safetensors.json","sha256":"921d814d6fa5d94be30a74b4d89543ec936c781ca926d18dcb20957bfdf57260"},{"bytes":510022,"path":".materialization/shards/model-00009-of-00120.safetensors.json","sha256":"a22c769d3ba85523734d01dfcbb5cc665aa92ee03544167b6d74efae28546391"},{"bytes":535915,"path":".materialization/shards/model-00010-of-00120.safetensors.json","sha256":"99f73edddd6b5156af945d26dd08cd48ab8fe313cefdc5d4e68aa06a89808864"},{"bytes":535707,"path":".materialization/shards/model-00011-of-00120.safetensors.json","sha256":"8429044dd8cb7908b21ed9b90e107e2f1a8590d504e6600426911ac699f98ea6"},{"bytes":510166,"path":".materialization/shards/model-00012-of-00120.safetensors.json","sha256":"fa143b20a1ea05fcc5628a1e930568c8011f5dd1511b46b5e640d511d5ae6162"},{"bytes":535891,"path":".materialization/shards/model-00013-of-00120.safetensors.json","sha256":"9b9641a47add516930a62bf976d0d7492d974d82b851945b56fecb92e1f8df85"},{"bytes":535147,"path":".materialization/shards/model-00014-of-00120.safetensors.json","sha256":"de0f74e000942557041bc0bcc74f72539f472d58827bbc6aeb8797b8e663abed"},{"bytes":510774,"path":".materialization/shards/model-00015-of-00120.safetensors.json","sha256":"0ef95d29638dd3035030d6b490eebd22b9daa83027377551f4681f540a9e0f81"},{"bytes":535851,"path":".materialization/shards/model-00016-of-00120.safetensors.json","sha256":"8677b1c192cb18073f84537ba0c3ae233620468e946679ff9e73db9f83514c0b"},{"bytes":534563,"path":".materialization/shards/model-00017-of-00120.safetensors.json","sha256":"aa9d28fb398294ff354dbf2d914110789f459ad245d851ee3ec377f208327cd2"},{"bytes":512936,"path":".materialization/shards/model-00018-of-00120.safetensors.json","sha256":"90fe4172bce89fd958b0e85868ff6d25b3bc1a811d5d6ba79ad7181c93e169ef"},{"bytes":535867,"path":".materialization/shards/model-00019-of-00120.safetensors.json","sha256":"7f77f080f0190dd73a3c74869fc8a8b4622e967afea7fe8964f42b28d775146e"},{"bytes":518560,"path":".materialization/shards/model-00020-of-00120.safetensors.json","sha256":"db733326f671217c5cad427e78f32ae5aa68f55267fe8c261927cb8c2ea31df7"},{"bytes":524020,"path":".materialization/shards/model-00021-of-00120.safetensors.json","sha256":"c09133ff8c45bf88f5cc85c3d6ef6bd29b10c522e71948849ade3ced2aba0429"},{"bytes":535867,"path":".materialization/shards/model-00022-of-00120.safetensors.json","sha256":"07416400c38b38ca3971e96f521700f45de531868d4ea4129aee6fdd037dc32a"},{"bytes":510030,"path":".materialization/shards/model-00023-of-00120.safetensors.json","sha256":"a4543fc7b6e65354b79087008cb0559cf089bbf9c656c88461a2ced2ef5e669d"},{"bytes":535851,"path":".materialization/shards/model-00024-of-00120.safetensors.json","sha256":"d784dcad595674f61f6f2e5368d193f20a0bebaf0d8b8b66d08d5ea0c5f68891"},{"bytes":535299,"path":".materialization/shards/model-00025-of-00120.safetensors.json","sha256":"739e95aef584be373fdf1cc5392062f55bfd6a51c56b0a0eed8f99601263cbbc"},{"bytes":510638,"path":".materialization/shards/model-00026-of-00120.safetensors.json","sha256":"cc822a4141b4fb1259cb7cf0019bd2767328a648055d93c3ac2d85ac65703614"},{"bytes":535875,"path":".materialization/shards/model-00027-of-00120.safetensors.json","sha256":"07009dae0fc9abf3c98eeedde578aca609a3d736a00dd1f66704fe5952748353"},{"bytes":534675,"path":".materialization/shards/model-00028-of-00120.safetensors.json","sha256":"ba4864837bf40411112c01fd7c5baf58e1a9772a39f8ef4b0b4ced269f964e46"},{"bytes":464627,"path":".materialization/shards/model-00029-of-00120.safetensors.json","sha256":"c8bb1835ddd126ba689ac711a8ae6d91a2ac6289e026c2e9f50db148f6220dd6"},{"bytes":535875,"path":".materialization/shards/model-00030-of-00120.safetensors.json","sha256":"763056215e677578fcd4b7eb2aafe7e20df03fc33abbe0fde1cb3d799d9aae3d"},{"bytes":534347,"path":".materialization/shards/model-00031-of-00120.safetensors.json","sha256":"0cf92f1415301a92d63dfacb26d58ebafe44f94609fe065fbc51be79280a621d"},{"bytes":511590,"path":".materialization/shards/model-00032-of-00120.safetensors.json","sha256":"a15212287887e7610f83a2a073b6291888bc70fc95a34d02ce2c3045643d9a13"},{"bytes":535851,"path":".materialization/shards/model-00033-of-00120.safetensors.json","sha256":"d882b1d207bd3e456a679df22ae1910df3b4c8db73a2cce28e25e40517d99d4a"},{"bytes":510022,"path":".materialization/shards/model-00034-of-00120.safetensors.json","sha256":"2d0abb1c9b502f3ad008244118fe45f761640231952b4ccacc6e0f5c01c05c55"},{"bytes":535907,"path":".materialization/shards/model-00035-of-00120.safetensors.json","sha256":"eb424f041ac17b7cceb62e6c98418cd5bbeb62b52c71ddc2a19583640f5d2f62"},{"bytes":535659,"path":".materialization/shards/model-00036-of-00120.safetensors.json","sha256":"81b1e3b999e8fed06b7308c08b17d71d46fcadbf41fba2a6507769102e4b1bbf"},{"bytes":510246,"path":".materialization/shards/model-00037-of-00120.safetensors.json","sha256":"604d281b96a22329c12501ba093d1588b82053cf259d7f1ce593025eba8fe534"},{"bytes":535851,"path":".materialization/shards/model-00038-of-00120.safetensors.json","sha256":"2b0087db11b5350b33dd5a6cd7858dedc6f14e01748f9559308c48b66cd80cc9"},{"bytes":535091,"path":".materialization/shards/model-00039-of-00120.safetensors.json","sha256":"6a783143193bf10e39118c68ad4e17744de2b005f1a14dc2fb20accc08fc841b"},{"bytes":512408,"path":".materialization/shards/model-00040-of-00120.safetensors.json","sha256":"9e89153f0dece501d3e4a2d2f9a513bd2c782e15a925cd893cd68393d255a14b"},{"bytes":535867,"path":".materialization/shards/model-00041-of-00120.safetensors.json","sha256":"ea9abf170718fcaf85858530d184f61eab6568938bb4958c0a2be1d7906782a6"},{"bytes":534451,"path":".materialization/shards/model-00042-of-00120.safetensors.json","sha256":"c401ff0d4ca1deb7fd58f324848f35c5b7ca3d2eab99a96710c3febbfa7ffa20"},{"bytes":511470,"path":".materialization/shards/model-00043-of-00120.safetensors.json","sha256":"c70d2d8c4c80486569f91e0b33d182249b038400d5ab03e7e2875f65dc7b5dd8"},{"bytes":535875,"path":".materialization/shards/model-00044-of-00120.safetensors.json","sha256":"6866929f97d6acf531ce8a92d08c5f5e59dc0c5913cfb31aebf77774318bb86f"},{"bytes":510070,"path":".materialization/shards/model-00045-of-00120.safetensors.json","sha256":"951c0e78d38c301fc1b4d1387d075b1fda33ab1551defda7052803a8291bf727"},{"bytes":535867,"path":".materialization/shards/model-00046-of-00120.safetensors.json","sha256":"2ed00e3532d42a5c41cdf74f912dc4152636c399798774136524ce0592454f23"},{"bytes":535747,"path":".materialization/shards/model-00047-of-00120.safetensors.json","sha256":"b67b04238eaf12d7b7ec03642c9bb20df835668bde3f4b4477464b457db2bbf6"},{"bytes":510126,"path":".materialization/shards/model-00048-of-00120.safetensors.json","sha256":"ec608572e29733b6a6027f61cbe84781cdef2c701ca2eea81fe482f1b937b77f"},{"bytes":535875,"path":".materialization/shards/model-00049-of-00120.safetensors.json","sha256":"79cbe9a141d8400912c1d4c7f7d58857e67c37e9f384af71ed15963e38ff8e2d"},{"bytes":535203,"path":".materialization/shards/model-00050-of-00120.safetensors.json","sha256":"d4c415f20d313493560175e2aff6bf84ea5e927d5d0ba7e7a672508560b9b90e"},{"bytes":512272,"path":".materialization/shards/model-00051-of-00120.safetensors.json","sha256":"f4c3aef067188c331a71882a5f099e8e397029a260c8c4a85234434b4320acf0"},{"bytes":535867,"path":".materialization/shards/model-00052-of-00120.safetensors.json","sha256":"1358ae41eaa3fa5620f35cb35629e3f429e8dc9af182b17e7f9a14dc53a6333d"},{"bytes":534595,"path":".materialization/shards/model-00053-of-00120.safetensors.json","sha256":"b0353f47312da53cd094feaaf187e411ffc228f19af512b2b96431dfbcd0cc30"},{"bytes":511350,"path":".materialization/shards/model-00054-of-00120.safetensors.json","sha256":"2f776186445cb810611b4e447257b8af7ccfd0a6580a5d16dc7055650dcbff0d"},{"bytes":535851,"path":".materialization/shards/model-00055-of-00120.safetensors.json","sha256":"fd3cbdf6b316ea46c54a52042d5d5b0547061ef08e7457a7f421a5d4c7bed0c0"},{"bytes":526243,"path":".materialization/shards/model-00056-of-00120.safetensors.json","sha256":"f4f824e1f264c65acfd0c5c3bc0e12400b836c0214aacbb5b7846857c3fdce2a"},{"bytes":515546,"path":".materialization/shards/model-00057-of-00120.safetensors.json","sha256":"410e062323aaee86563d85cd1f79c6729c1b5431acd8891ba7bc851ee4ec468f"},{"bytes":533315,"path":".materialization/shards/model-00058-of-00120.safetensors.json","sha256":"55588c9e6ae8303db9def20956edf71be1f38347f3db293319c1ac5b381a4667"},{"bytes":509640,"path":".materialization/shards/model-00059-of-00120.safetensors.json","sha256":"83b87d82fa6a66a9d851f97dd97af69f8cdbc4fdc711bb390d68b0ef9062de9a"},{"bytes":535851,"path":".materialization/shards/model-00060-of-00120.safetensors.json","sha256":"f5c286aa7c6274412a8a51412b17fe3ebe8c9e2e751fe17ea61854320b986dc9"},{"bytes":535323,"path":".materialization/shards/model-00061-of-00120.safetensors.json","sha256":"d4c780a921de809dddb8ec92b6d9b7396c139ee6aa15822bc232edd21b7b0751"},{"bytes":510622,"path":".materialization/shards/model-00062-of-00120.safetensors.json","sha256":"d28dca2459ae348608e5a2ad1582add754a6135e03a29f6a66cd529abc3335cd"},{"bytes":535867,"path":".materialization/shards/model-00063-of-00120.safetensors.json","sha256":"3ce37e9b9c75627ec0046b805cf7bc166a028fc9cd8f4ae27870e32a19ed347a"},{"bytes":534699,"path":".materialization/shards/model-00064-of-00120.safetensors.json","sha256":"a2e7b7ce8aedea8c7f1b79b0d4eb8aadc62f935ef544ebe961e3bf22ddb745ad"},{"bytes":512776,"path":".materialization/shards/model-00065-of-00120.safetensors.json","sha256":"50e8cebcdac5f20d2626b6eb4f65a45db2fc2da0b196d09122b8b3bafc71bc4d"},{"bytes":535875,"path":".materialization/shards/model-00066-of-00120.safetensors.json","sha256":"db66bcfc88353450e4e76689a86036d1171c06eb0b153f26598ba0ba36fd148a"},{"bytes":534099,"path":".materialization/shards/model-00067-of-00120.safetensors.json","sha256":"99ca7773e9e74fe1da2d149eddf3060c14df4a22d4b0947b6dfc6ada1072c022"},{"bytes":511838,"path":".materialization/shards/model-00068-of-00120.safetensors.json","sha256":"3981caef5a222ff73a55a306895252f8418dc2560fc53ccb1d8816c2f95abbe5"},{"bytes":535851,"path":".materialization/shards/model-00069-of-00120.safetensors.json","sha256":"647307884644c56ea8e95e66cb7bb478e89df2247a058aadbe6528fd4e8a1627"},{"bytes":510022,"path":".materialization/shards/model-00070-of-00120.safetensors.json","sha256":"61fc4536e257ed876df79998c4468c5d97b7927b13ba31d9567202cd74f92e4c"},{"bytes":535875,"path":".materialization/shards/model-00071-of-00120.safetensors.json","sha256":"cf6dba6de255e800f25de85b31f4b0df5f09710b187e5b3b499d97caa032497d"},{"bytes":535443,"path":".materialization/shards/model-00072-of-00120.safetensors.json","sha256":"edb9724f88e0a18f495e74c72ecf8a801dc8093bef7dca7797ea98de3d8ae32e"},{"bytes":510494,"path":".materialization/shards/model-00073-of-00120.safetensors.json","sha256":"3d397578ba4920e9427939afaabdac25b80162270fbed341a9d600889063ba94"},{"bytes":535851,"path":".materialization/shards/model-00074-of-00120.safetensors.json","sha256":"161d74aa4d598927b4bd23b952a8a8c17ef0a1ebdacd6a4120f0947e837dfef5"},{"bytes":534843,"path":".materialization/shards/model-00075-of-00120.safetensors.json","sha256":"5f1876dbf3556d818d988c49b9ed6e22b93a5c249865d48a6d7d002aad54e2ff"},{"bytes":512656,"path":".materialization/shards/model-00076-of-00120.safetensors.json","sha256":"bf46b5a32e3361922ec127ae2ba06469056d9f610f0f135e0c47e5f7f9ca0314"},{"bytes":535851,"path":".materialization/shards/model-00077-of-00120.safetensors.json","sha256":"1ffb769cb74f6e848a90977b96b5fcec66e00e8ba23f5c1a4fe442288eef2493"},{"bytes":534219,"path":".materialization/shards/model-00078-of-00120.safetensors.json","sha256":"ff215ea0a4fa6230abf2712ec63f17c6f672dded395bb3531d8c211abf5a4304"},{"bytes":511718,"path":".materialization/shards/model-00079-of-00120.safetensors.json","sha256":"35c16d1ff4827312c2cd40e435bddc03a803e9b1a08d898987e3d9e673423715"},{"bytes":535875,"path":".materialization/shards/model-00080-of-00120.safetensors.json","sha256":"1500c89f765f2807c52c408b0bd72826d50c6eb2cec030521b3325430bd0ad2b"},{"bytes":510022,"path":".materialization/shards/model-00081-of-00120.safetensors.json","sha256":"524b750e2684840aa95e06f7fed8aab4a6908cb019ef6cf010b764b2b0458102"},{"bytes":535851,"path":".materialization/shards/model-00082-of-00120.safetensors.json","sha256":"3febf8cefa4f8a0823ce773190435a80863bbd721a7dcac20b74644b4b18618e"},{"bytes":535563,"path":".materialization/shards/model-00083-of-00120.safetensors.json","sha256":"bbf37f5ddb8490fd85ddf3df51a0c0ced34b928464bb9b10a271cc335c6b83d6"},{"bytes":510374,"path":".materialization/shards/model-00084-of-00120.safetensors.json","sha256":"0f9981fa883c348642c735d87809908ad7eccad414c0deae29f6fa6e3dc8c638"},{"bytes":535875,"path":".materialization/shards/model-00085-of-00120.safetensors.json","sha256":"cefe465f7910a51bd80efa3ece377dd6d75b8b3f6a694350160ef207da458cf6"},{"bytes":534939,"path":".materialization/shards/model-00086-of-00120.safetensors.json","sha256":"c0949f39be57d6de4ca1ea4018fd6bfec22f388f3a66bca809582f845b790668"},{"bytes":511026,"path":".materialization/shards/model-00087-of-00120.safetensors.json","sha256":"a22a40e918551b7e1ebb4e3bca3cd96c79d24aa76906b40d7a819e96b1eea58c"},{"bytes":533291,"path":".materialization/shards/model-00088-of-00120.safetensors.json","sha256":"ad009d666b4fa9715614d90261b210e9fa97dc96a12efefec36c9519c818aeac"},{"bytes":531803,"path":".materialization/shards/model-00089-of-00120.safetensors.json","sha256":"068da6054f6be308f6f6fa97239a7f7fea46a49d4bb371970a5d2356555c60fb"},{"bytes":511260,"path":".materialization/shards/model-00090-of-00120.safetensors.json","sha256":"a1b3e5529b0e1474ee6375c536104fe5f72a19ea6a59960ef6a33cdea5beab8f"},{"bytes":535851,"path":".materialization/shards/model-00091-of-00120.safetensors.json","sha256":"3c7f9a4ac82c180b29d8c25ae35a51df8767f515dcac9c67dd782a249e697108"},{"bytes":510022,"path":".materialization/shards/model-00092-of-00120.safetensors.json","sha256":"33d7d5fe815f600493bde03df7442d7b2cf1f4d39ad26e01c6f7ea97332181b1"},{"bytes":535875,"path":".materialization/shards/model-00093-of-00120.safetensors.json","sha256":"36fd776f87fddafda48ef9a99dd65f0c7beb0fb09eac8a33babd33fa423f5c59"},{"bytes":535683,"path":".materialization/shards/model-00094-of-00120.safetensors.json","sha256":"405cb805af678eeeddc4423f5cf9449a3ab3a445ae818a649180b0b25cdfd165"},{"bytes":510254,"path":".materialization/shards/model-00095-of-00120.safetensors.json","sha256":"2e6283cb01dcaf4030d1ff3ce4f25d94fe0bb648783ac71cc51b4b6d2590322e"},{"bytes":535851,"path":".materialization/shards/model-00096-of-00120.safetensors.json","sha256":"73828ad22dd3011d9df1f0d52f3d0c8561e7a4d45ca86e558551b8d9f8f00fe2"},{"bytes":535059,"path":".materialization/shards/model-00097-of-00120.safetensors.json","sha256":"a2d06e799b3f03f35c8566696d08f3fc8a1572de9de51a046754adc05e3bfa44"},{"bytes":510886,"path":".materialization/shards/model-00098-of-00120.safetensors.json","sha256":"71ca94c8d0d7df0d1eab2a1d1e9a45ca251c18abdc3f6b3c746374bf8aa644dc"},{"bytes":535867,"path":".materialization/shards/model-00099-of-00120.safetensors.json","sha256":"2a02fb2af3083610f3fbe051a641a3c8c4fb80e6d0e4ce24331bff1612924616"},{"bytes":534451,"path":".materialization/shards/model-00100-of-00120.safetensors.json","sha256":"f5448b63555d18468904a19591ceaee14f4885f92c29cc8b7d74bbfee66353cb"},{"bytes":513024,"path":".materialization/shards/model-00101-of-00120.safetensors.json","sha256":"f99a0c865757ceb105d64d44724bc997d818978e633b5f89f42cc53ee39a3348"},{"bytes":535875,"path":".materialization/shards/model-00102-of-00120.safetensors.json","sha256":"36fecc968b5d60c7e9d7a26b0c3acb0c7829c7150eb1a08403e7db2e0987b166"},{"bytes":502500,"path":".materialization/shards/model-00103-of-00120.safetensors.json","sha256":"7a739335a53fc49fe54371e9aa9d8d40df8d746cbd9548f11ca4cce92de2496c"},{"bytes":535819,"path":".materialization/shards/model-00104-of-00120.safetensors.json","sha256":"eefb364739fb126393e649631845bba544c71b24bd5f220e198149ab8b4760a4"},{"bytes":535771,"path":".materialization/shards/model-00105-of-00120.safetensors.json","sha256":"67cac41752379d844efd1fcdec4ea8e4c302e241302660727e24827fd2964365"},{"bytes":511365,"path":".materialization/shards/model-00106-of-00120.safetensors.json","sha256":"57734229ac3b02140e513d0bfa81723b25d8bb17856f45a6833dee00ab6bec35"},{"bytes":533315,"path":".materialization/shards/model-00107-of-00120.safetensors.json","sha256":"7e44434bae18c4bab9e41760c5b4e784430c153162b4c2c13deab19df629774a"},{"bytes":532643,"path":".materialization/shards/model-00108-of-00120.safetensors.json","sha256":"5f561d9d726b3b114088ead3a8a4a069399d037bfab5ae9c38a0db06089f7c96"},{"bytes":508262,"path":".materialization/shards/model-00109-of-00120.safetensors.json","sha256":"ea22c6234feb136188ee0c31d7a3ec5bc12944940da201ed3d11234b91805dd5"},{"bytes":533315,"path":".materialization/shards/model-00110-of-00120.safetensors.json","sha256":"45f4f539f8d355d7b1d4362cd0e95d321c09aa3aba7b764dc453a49e106b1927"},{"bytes":532051,"path":".materialization/shards/model-00111-of-00120.safetensors.json","sha256":"f96e849ada435b0b92876f36d923417fff687509a0bb4accdc235899c480bbe2"},{"bytes":508870,"path":".materialization/shards/model-00112-of-00120.safetensors.json","sha256":"4a5064c1a21905ac00b00a48e7fe5bec3fad496beaec9e9491992a4b690e4fe8"},{"bytes":533291,"path":".materialization/shards/model-00113-of-00120.safetensors.json","sha256":"2abe6623a705200afca4e58c08c6f9a067fef20d174075783a3581b81e03c6e7"},{"bytes":528567,"path":".materialization/shards/model-00114-of-00120.safetensors.json","sha256":"f792a91136ab2bd70bad63331adcda728fb3a2eb71dfd886d16aae7d38b2b5f0"},{"bytes":513887,"path":".materialization/shards/model-00115-of-00120.safetensors.json","sha256":"970721ee8cf97f5d2fa1d66f1aa0eec1011cdfb83c60851824126fd7e2abb84a"},{"bytes":533331,"path":".materialization/shards/model-00116-of-00120.safetensors.json","sha256":"9844a9640265951c0817114d290380acaf92a9a7f2dcaa124800162e2fe38dc0"},{"bytes":507566,"path":".materialization/shards/model-00117-of-00120.safetensors.json","sha256":"3e29504486877265861ffb6e5653984c9ed21eb21a2f0b0b1d777cfb9503b799"},{"bytes":533291,"path":".materialization/shards/model-00118-of-00120.safetensors.json","sha256":"8be4ecc40a57e7a2c59f444d4621c64548eddd6314aca3dcabf20ad2891cb88d"},{"bytes":532763,"path":".materialization/shards/model-00119-of-00120.safetensors.json","sha256":"e16b7191616ff1effa78859b5ea9e5db9e9f516d13292a37a4850848f757e2fd"},{"bytes":384527,"path":".materialization/shards/model-00120-of-00120.safetensors.json","sha256":"14e78d614464c3395ed1810b5c05d553cb3031b3ae77269babb787936a80d613"},{"bytes":1070,"path":"LICENSE","sha256":"30b85b6b9659f2e78aa259f8faf5d920a68dee7c9ced3fa6dba1f19f2bc4fca1"},{"bytes":3157,"path":"README.md","sha256":"a24ed04666047362a7ca6b8798453996631e977633f2e3b10b5ad1aaea815750"},{"bytes":8617,"path":"chat_template.jinja","sha256":"41cff9af7b3a86c96751b107a8444f245fbda0bd5320b636a5bb1f7f4ba1a5c3"},{"bytes":6220,"path":"config.json","sha256":"4f5341e048984459471bfb9c894e6bf87e69b9c67402672af901631d1349f265"},{"bytes":943,"path":"exl3-mcg-storage-abi.json","sha256":"c51ff4803f8d0bc69b2ef57ec2f5d72129a5e28e0161221ffd2977eba35a0e69"},{"bytes":194,"path":"generation_config.json","sha256":"230c30609ecbbb9e6583bedde8e7bdda0c6eb8fe5fad0eaeb3d1b293d751cb4f"},{"bytes":26933,"path":"materialization-receipt.json","sha256":"afe588284702c0676b7af5df48bc0e0568bb42b1821808ab71c6dfa1f0c48b61"},{"bytes":4113768668,"path":"model-00001-of-00120.safetensors","sha256":"46cc9e99897500fee92f2f08c95665cda51a704f835548465a1023cd4db63ef7"},{"bytes":1346273248,"path":"model-00002-of-00120.safetensors","sha256":"b78eebdbee138150d310d7c655baae1ea31720b8ad53f39389b1679ce0f51552"},{"bytes":1346272832,"path":"model-00003-of-00120.safetensors","sha256":"d377c4b645e7283f21370a1d12d90f238ec38194f78ddc483aea6be45c0db2c6"},{"bytes":1591900800,"path":"model-00004-of-00120.safetensors","sha256":"65f841ba0f3449b18af49eca276db6047a78088a3ff293f9edd41dcb4082af65"},{"bytes":1346273256,"path":"model-00005-of-00120.safetensors","sha256":"d0425becf2a42244527553a983b269e2656521069c6e9467d0ce6912dd2fee80"},{"bytes":1346272536,"path":"model-00006-of-00120.safetensors","sha256":"3f6ded63fe3fe96a176c1ffad431c2e7350307238a89fb89ac3947605ea4d72a"},{"bytes":1570455356,"path":"model-00007-of-00120.safetensors","sha256":"a8e2cf2d869afb825934ad43bcbbf7e16132bdfca8443ca52a39c46b36f775cd"},{"bytes":1346273256,"path":"model-00008-of-00120.safetensors","sha256":"eaaf3e209d6bcd37efae31e10fbbc822755629a701076ad9a527d80c216810d5"},{"bytes":1591900360,"path":"model-00009-of-00120.safetensors","sha256":"ad30bcac854cc303563eb2f280439bf86d7b56c81a43e00175fecd55d63f9905"},{"bytes":1346273272,"path":"model-00010-of-00120.safetensors","sha256":"35530edfbce782687f3b06b85f85f34decc537a53015c0bee1a4caf82c7cc78b"},{"bytes":1346273168,"path":"model-00011-of-00120.safetensors","sha256":"686f13c8151614b4d85c02c20e8a786bdfa1eac82d8260bd8d877833db346288"},{"bytes":1591900432,"path":"model-00012-of-00120.safetensors","sha256":"79a19881d8a37ac0afbcd0b0e77ea45150900467b43ae0b412288ce82154a718"},{"bytes":1346273264,"path":"model-00013-of-00120.safetensors","sha256":"d07940d68fc7b90f99eca406819e4bcd94995a909e32f0f3d15f3251c8f06552"},{"bytes":1346272888,"path":"model-00014-of-00120.safetensors","sha256":"ca3290268d136656a2b8eb56328fcf275824bcd60c269a39d9b1ee02d29d42fd"},{"bytes":1591900736,"path":"model-00015-of-00120.safetensors","sha256":"2172d6d1c76e4d79d0d6147e693080a1ee2fd2761518764ea7ae42d6e21816ca"},{"bytes":1346273240,"path":"model-00016-of-00120.safetensors","sha256":"b33392a8b5f66e82115d1c7bbe830945fdda9b5b3a2d5094c9e8f7120186d09f"},{"bytes":1346272600,"path":"model-00017-of-00120.safetensors","sha256":"b198f9d0606953932f131b3d1124488702280ca55627d5c9156823a87a56144b"},{"bytes":1570455308,"path":"model-00018-of-00120.safetensors","sha256":"b98e6fa60d19eeabe5f937dfa5cde62ee0cffb7e8687f7c6e8e54be797e76cfd"},{"bytes":1346273248,"path":"model-00019-of-00120.safetensors","sha256":"1ac4f98e4a16f9f620d83a51353a5b3ae4af05055b7777633981a930f063a9a4"},{"bytes":1485484468,"path":"model-00020-of-00120.safetensors","sha256":"db5f5e4bfe9704b55cad6fddcbeb0b50c0f0d760af247f13ea78e6b9a13bf5e9"},{"bytes":1444278044,"path":"model-00021-of-00120.safetensors","sha256":"94abdb9997af61ed92f43d58c73c96947add0ac1580c2255eaba2b254aa34782"},{"bytes":1346273248,"path":"model-00022-of-00120.safetensors","sha256":"d1c09ce5b40bf2f976a6918188b10382735be8f9d936fc7d9e3d174d67fcb193"},{"bytes":1591900368,"path":"model-00023-of-00120.safetensors","sha256":"e75296748bb0940bf8a45e3743444d46404a0984c25164212764211d07ea5c3a"},{"bytes":1346273240,"path":"model-00024-of-00120.safetensors","sha256":"f9fa4d4312b74a757befe4f488dab56370a9c21771a86c2b92e3ac74ed068863"},{"bytes":1346272968,"path":"model-00025-of-00120.safetensors","sha256":"6f94c10adb857a39f5535be2b50b8037d08c798f13df4eb934fb9c14d44e9962"},{"bytes":1591900672,"path":"model-00026-of-00120.safetensors","sha256":"0c65f0517b61444c2b9c243ed5a97a6b2aec76054da36c2ef23b7d0263d0f1f4"},{"bytes":1346273256,"path":"model-00027-of-00120.safetensors","sha256":"af324d2631116bb4ca61b15578ac270874ce82035cb262e1adcb4056a76841b0"},{"bytes":1346272656,"path":"model-00028-of-00120.safetensors","sha256":"5e031de67c3c86ca62170a72c996c1cbd588a3a36b5a026dd0823e088793f196"},{"bytes":2006477724,"path":"model-00029-of-00120.safetensors","sha256":"bae837ec87eca4b857597783fc9035d4fa6e049addd4a1f46c052de1a31bb6da"},{"bytes":1346273256,"path":"model-00030-of-00120.safetensors","sha256":"bbe67349b26057fe942bfce9fdff6422a9b822924b2445bbde7c2ca4c974c707"},{"bytes":1346272488,"path":"model-00031-of-00120.safetensors","sha256":"3aebbcb6ef80e74313e015c5ce828bb09fb02d65256e2567bf6bee2d1bdcf887"},{"bytes":1591901144,"path":"model-00032-of-00120.safetensors","sha256":"cb9063b4790ac8ede704b801aabf416adfbcc2cb4c3cece85d499ffd710b5f00"},{"bytes":1346273240,"path":"model-00033-of-00120.safetensors","sha256":"598e794cb00fcb85f20ff6e701e5a98a601b19681d77c11c370081b8836d0417"},{"bytes":1591900360,"path":"model-00034-of-00120.safetensors","sha256":"fe68ce83f4b32da0cdc5f6803ae4ac977f2e0b007df627dce80ac93b3fd88817"},{"bytes":1346273272,"path":"model-00035-of-00120.safetensors","sha256":"5c439c150741aa601c9f8f8a8f0bb650a59deb9d66e018e3e003baf97551a0e2"},{"bytes":1346273144,"path":"model-00036-of-00120.safetensors","sha256":"bba2d7a1df63672dd6400c74bd07352698bc9e74497812963d76f6c379a4fcb7"},{"bytes":1591900472,"path":"model-00037-of-00120.safetensors","sha256":"39ac664b9906d0970fedd207c20fe8b54cd16703ba765db4de332f2f3a4ae24f"},{"bytes":1346273240,"path":"model-00038-of-00120.safetensors","sha256":"26b70b8b61940befb31f4bf5956dfd9dc89500bddb36b4e79eb046ab39000412"},{"bytes":1346272864,"path":"model-00039-of-00120.safetensors","sha256":"c2599dd1cdc04c23c3383ecccad7d50d11d0e52c6602574bc1da2428e84f7028"},{"bytes":1570455044,"path":"model-00040-of-00120.safetensors","sha256":"e109dbf65aba7827809cc67334065364a1684715179b31311e0a31c00d8dd48c"},{"bytes":1346273248,"path":"model-00041-of-00120.safetensors","sha256":"bb85b908911687c0e276e43e81b5a4a265a87c00965324532681097ca491bc9f"},{"bytes":1346272544,"path":"model-00042-of-00120.safetensors","sha256":"a017885e30064729e5f70cbaeda434663c6c382aa9d2ba2a73f820a8cd5b6bbe"},{"bytes":1591901088,"path":"model-00043-of-00120.safetensors","sha256":"04f3aeccb23c779ac9e6be9e952418b95cac19327e7873dce5f69e4ad728f6e0"},{"bytes":1346273256,"path":"model-00044-of-00120.safetensors","sha256":"fa5a385f7a8220ed99bc081f2fe26a07c480a3da7f497be57cf95f96ceb1c34c"},{"bytes":1591900384,"path":"model-00045-of-00120.safetensors","sha256":"b0b508dbd6217fe861a68bd8890415fc0f97404ed9ea7155adae9476b12c27d0"},{"bytes":1346273248,"path":"model-00046-of-00120.safetensors","sha256":"c0e27a8d0a9e4521e96f17ad0e40de8152ddd8c26f7739520cc052069e4432f7"},{"bytes":1346273192,"path":"model-00047-of-00120.safetensors","sha256":"5c471e0726598af910a1d5c895eca9bf41afe1e45311ee0dc962572b21097f2d"},{"bytes":1591900416,"path":"model-00048-of-00120.safetensors","sha256":"1d957114322b3d9dbb4cca73d9bba9cf628ddc7f5a6dd1d6af6224eb6aff003e"},{"bytes":1346273256,"path":"model-00049-of-00120.safetensors","sha256":"895d22c61a559667d84ac65dcd3012de18103738d3660c0c84db0b90e29c7e32"},{"bytes":1346272920,"path":"model-00050-of-00120.safetensors","sha256":"c2bd099b71e35b8af36785f23e025c8b3ce393197b801cd3e53ee33e9be71531"},{"bytes":1570454972,"path":"model-00051-of-00120.safetensors","sha256":"1c30583b2edd80ed55db4086e1b846f7a9de54f7996bfa07e2ac0039f702c10a"},{"bytes":1346273248,"path":"model-00052-of-00120.safetensors","sha256":"7c800f2f476cff08606cc3b036571d43ea501518fcba658aeda5db0fce815346"},{"bytes":1346272616,"path":"model-00053-of-00120.safetensors","sha256":"43acf4bbb16dda6f5a7c31240108bad19c64ac5eb9d20022321c4eeea48bc991"},{"bytes":1591901024,"path":"model-00054-of-00120.safetensors","sha256":"98bb70809af0d4d3177a377eefcbf728dd7fbd9e3891f130c05c66d29cd04860"},{"bytes":1346273240,"path":"model-00055-of-00120.safetensors","sha256":"285c605b3391fbca1a40f28fdb6c1856a155ccfe6de43eb59e11033360dadf51"},{"bytes":1439345368,"path":"model-00056-of-00120.safetensors","sha256":"6f825c25cd071930e7cbec832fe71a65767024b6853bdf554a9dd6534d0cd25c"},{"bytes":1494623036,"path":"model-00057-of-00120.safetensors","sha256":"9c7e6d5fd6d4138fde51eef51a83ff8a33424294c44d52d1b62b42ddb79ada4d"},{"bytes":1346271976,"path":"model-00058-of-00120.safetensors","sha256":"0b73d4dc1f373046f08a000d60484a7cf5335c4b0a1e4588d0cf8089f3ed75f2"},{"bytes":1570453660,"path":"model-00059-of-00120.safetensors","sha256":"c5e83b54c3f1912aa5fd35f1c3170415f54708887f6f358cb1eac0b93391394d"},{"bytes":1346273240,"path":"model-00060-of-00120.safetensors","sha256":"c37b7e82d16a82243664ecac9e417a815195e1c26d9affe2ce681bffcb2fa046"},{"bytes":1346272976,"path":"model-00061-of-00120.safetensors","sha256":"ec26e65bf26f2dadf4e4c7aeefe48acbd9b677a3d03ba0d3229f1cc796647bb4"},{"bytes":1591900664,"path":"model-00062-of-00120.safetensors","sha256":"98c6427e9b22529ea985aa5d343e24055d3fd570b866e4d8dfb8f0b9470caedb"},{"bytes":1346273248,"path":"model-00063-of-00120.safetensors","sha256":"dacdd78b8a4f74ea5502d28b0a8d0c632a05252e095abc6245f0a63bdbd57a46"},{"bytes":1346272664,"path":"model-00064-of-00120.safetensors","sha256":"7f134d623d5f229cb4732905ceb269404e1b00a72b8c25cc4f09d1e8610aabc5"},{"bytes":1570455228,"path":"model-00065-of-00120.safetensors","sha256":"16661291a3d218f765edcb3831ff9426af21d6d56e6d8082b194e5cac9348109"},{"bytes":1346273256,"path":"model-00066-of-00120.safetensors","sha256":"e8c5392e733bd98d5d9f38d47e18cf6834bcbc0b8d24937c42d36ebc60604910"},{"bytes":1346272368,"path":"model-00067-of-00120.safetensors","sha256":"f2fb7876facab2bbb569fe94ab41dd1e71c50a3811853bf37688e5ebdc5813c0"},{"bytes":1591901272,"path":"model-00068-of-00120.safetensors","sha256":"abc3009f10522e97eedbcd34903e3aafa55f7d72efa4ff3db4b5c4fb28259ca1"},{"bytes":1346273240,"path":"model-00069-of-00120.safetensors","sha256":"ee4f3cb631ca5b0552fab89a3ef05ed0d8dab5ee0066fa245af479c962071e53"},{"bytes":1591900360,"path":"model-00070-of-00120.safetensors","sha256":"8b60971fe302e1bcf1bb7da900276a3f26fcd38b10d6760330ee59ea8df7a589"},{"bytes":1346273256,"path":"model-00071-of-00120.safetensors","sha256":"9fb9f3581b0a09d43e94e0476b9fa0971b3ab30b3d980bf6bb0814fb92db82d4"},{"bytes":1346273040,"path":"model-00072-of-00120.safetensors","sha256":"caec81374ed646f0d7953f81565438ce18dfc11197cbb1a442b557cfb907dd23"},{"bytes":1591900600,"path":"model-00073-of-00120.safetensors","sha256":"313e11a85717e5fe56c6901f16c10aab760619b7655d355185a17dcb6986397a"},{"bytes":1346273240,"path":"model-00074-of-00120.safetensors","sha256":"3c22a6012bb34c264869fd8a93ac019175f22d9d255cdb1987d25572f6ebcb37"},{"bytes":1346272736,"path":"model-00075-of-00120.safetensors","sha256":"55992a5b643dad0a06c6bd8e4a13bac8980d31f3e773d56cc86a0c36543c02e5"},{"bytes":1570455164,"path":"model-00076-of-00120.safetensors","sha256":"3dea15fd98ef8c45a23876a3dd1f3f6c06feb6c5e3a35b29dde93a82196e9f63"},{"bytes":1346273240,"path":"model-00077-of-00120.safetensors","sha256":"fb6efae4033a54282fa0f779ce41dd64edff903621e829e23c28f2423f032059"},{"bytes":1346272424,"path":"model-00078-of-00120.safetensors","sha256":"1f231a1c8149307a14c940df734637e03b0cad5db40e4165c69b9a5e817471d9"},{"bytes":1591901208,"path":"model-00079-of-00120.safetensors","sha256":"84eb7df2440c3fe0cad633bfdb05fc1f9f4a69514d33a3c48e08f751e8729b03"},{"bytes":1346273256,"path":"model-00080-of-00120.safetensors","sha256":"1008d2f310da3804fe85ca18c86f7f40c7e7005afe3edd0e17bff9207edb15cd"},{"bytes":1591900360,"path":"model-00081-of-00120.safetensors","sha256":"30bb97e87009412ec4445773ab5dde2267f395290a37ea204b0b94fb78b06c16"},{"bytes":1346273240,"path":"model-00082-of-00120.safetensors","sha256":"6314d366c0a954e7a8beed9ead7389dc5c9ec6a78667f63778edb68a1ec5a5a7"},{"bytes":1346273096,"path":"model-00083-of-00120.safetensors","sha256":"424127fa0a70e70d5073200c3ede1097dc31345b67309e9c1ca14fc1d2c601c9"},{"bytes":1591900536,"path":"model-00084-of-00120.safetensors","sha256":"8f5be1a45eeafd520fa3e0adceffb15cceaa6510c61d032475f017966885c248"},{"bytes":1346273256,"path":"model-00085-of-00120.safetensors","sha256":"22722c615db28a151620c8a6d11714f6de26bc386f3d495a89666191122ee1bf"},{"bytes":1346272784,"path":"model-00086-of-00120.safetensors","sha256":"dcd025dc9e48dd3744996a3b9d337d3c8636c46591e80ab6cd489aac78b61b76"},{"bytes":1570454348,"path":"model-00087-of-00120.safetensors","sha256":"c00bdc2031dd9c042fcf56613979d029256c2c337c6dddf61f6e5efe18e6c883"},{"bytes":1346271960,"path":"model-00088-of-00120.safetensors","sha256":"147abc2a72e6eea5941cd03175febb962a81d068a2872c6594bef6f3e5535007"},{"bytes":1346271216,"path":"model-00089-of-00120.safetensors","sha256":"5d2ec3fd271c7a2ec4161f4e5bf2c14eef7f549ae1a6d39bb666614e26d2dfc1"},{"bytes":1591900984,"path":"model-00090-of-00120.safetensors","sha256":"2bb2e9ab785905faaeeb8b6a3df7ff2f7ae246fb1e3f51fd4cfd0b3af94e44f1"},{"bytes":1346273240,"path":"model-00091-of-00120.safetensors","sha256":"8ccbc08c9dbcd37d1ec6cfd772667b12cd521bfd4dc05113e7e9f0ce1d2b9603"},{"bytes":1591900360,"path":"model-00092-of-00120.safetensors","sha256":"20422d9ea7e0997852897daf8e6c84b420c9c8b3a700b301f416091cdb8e4efb"},{"bytes":1346273256,"path":"model-00093-of-00120.safetensors","sha256":"753329dbb8d2b4efc7cf4ca7fe9b1455e0128cbe725a98fc91a05fcf38d616d9"},{"bytes":1346273160,"path":"model-00094-of-00120.safetensors","sha256":"f08e09cb29e5c1ffc05b128d581e815914ced8f30949a1e2e114470dbbba2dbd"},{"bytes":1591900480,"path":"model-00095-of-00120.safetensors","sha256":"c08536722ef0a2cecb5a5d843fd0086275c329918eff733676af1cb6bdb99abb"},{"bytes":1346273240,"path":"model-00096-of-00120.safetensors","sha256":"fafe3a925e795c9b570c8eefeb0ea492df97e941f23e2e7e8206ded8cd28933d"},{"bytes":1346272848,"path":"model-00097-of-00120.safetensors","sha256":"e616de880c9587930ecf502799255189c0f03e102273ade47dbc104c136e92c5"},{"bytes":1591900792,"path":"model-00098-of-00120.safetensors","sha256":"7c34ac45ffba12a049c0b563cd002a50d1fb6ea9dc80c6c23203c43d5768705c"},{"bytes":1346273248,"path":"model-00099-of-00120.safetensors","sha256":"9693db794a7992532a96bd0ea185a49a76c123fb50f7bcf4fff0902847e0c6ed"},{"bytes":1346272544,"path":"model-00100-of-00120.safetensors","sha256":"a19fb303a9814d5082fc3fd5d69127bc6724bf574276e6d18918b56ab46e8eee"},{"bytes":1570455348,"path":"model-00101-of-00120.safetensors","sha256":"c24ca6ae7ce061eba28d302b878700d30ec22656a69a3db53bbb4ac4ad1ae961"},{"bytes":1346273256,"path":"model-00102-of-00120.safetensors","sha256":"b5c8396800acefb6d0bc7a0a48cd055d28faa94dc2bb34f1867a58ec83991028"},{"bytes":1640623840,"path":"model-00103-of-00120.safetensors","sha256":"09139292ba5af27e66767a6860a53b7693f091c513bac154c32d44b408783738"},{"bytes":1346273224,"path":"model-00104-of-00120.safetensors","sha256":"b0af3c5b38f078c72d405319fb47ca4c7ef74324266106ca3962ae63dc6e0a28"},{"bytes":1346273200,"path":"model-00105-of-00120.safetensors","sha256":"f475efcce530b46749e9ece468b65a5aa65a073b7fcec00c22aec5b02e4e7782"},{"bytes":1570462668,"path":"model-00106-of-00120.safetensors","sha256":"1059fe4a53c233b1fdb28ae431d61190c6491f55223c58c0660dfead53be7bc9"},{"bytes":1346271976,"path":"model-00107-of-00120.safetensors","sha256":"57e1f99044388f5c0e5b422d678e9477475dc170dcc97b654f931e3ef9facfee"},{"bytes":1346271640,"path":"model-00108-of-00120.safetensors","sha256":"286de5d27896da595638cc5aae2e390bd7676e0d450f490842c165975febff5f"},{"bytes":1591899480,"path":"model-00109-of-00120.safetensors","sha256":"b41955ae5e684cff92ae005d27b626b2abbe72fc1ad0e9b273b6d71ca3a74b66"},{"bytes":1346271976,"path":"model-00110-of-00120.safetensors","sha256":"b2bd23f9b6d11910f9b7f9e4951ae254bc64c1844a2541de48cd4ac6755d0abc"},{"bytes":1346271344,"path":"model-00111-of-00120.safetensors","sha256":"091e1d588b899c5369b713a053a7bc76ad35bc93a81af20961167f89bc953b1f"},{"bytes":1591899784,"path":"model-00112-of-00120.safetensors","sha256":"8a7c2486aa93f1c952da2ffd6137f2f38b443e21923c5f640edcaab6c340d4a1"},{"bytes":1346271960,"path":"model-00113-of-00120.safetensors","sha256":"8f3205a5dd7e9162c2ec89e19b82c10fcdd41f716b101322833f33cc27aed753"},{"bytes":1397079148,"path":"model-00114-of-00120.safetensors","sha256":"e3cf6f9ea4e07df98a3bc4cbac85f9228ff3e9fd45f02f8735672d27f81a9404"},{"bytes":1519648072,"path":"model-00115-of-00120.safetensors","sha256":"347b2e08fca7a5bbe63e39e2bd225fb0af5a7ff318120cf7e5289c4ee2a72c23"},{"bytes":1346271984,"path":"model-00116-of-00120.safetensors","sha256":"3d9c0120686a8db80f7c6bd5c1044e737a4a8f5dc8f4111038308aa0cf80d88f"},{"bytes":1591899136,"path":"model-00117-of-00120.safetensors","sha256":"48eca9375a20c8f8c6435ea6e4ee709238680873967408dc0ba499179e7b7da5"},{"bytes":1346271960,"path":"model-00118-of-00120.safetensors","sha256":"f7df8825ffec03ac813ecfabc62f9a416c8884d11aa35da655f322a6b6870817"},{"bytes":1346271696,"path":"model-00119-of-00120.safetensors","sha256":"8e393cfce320eba98c19f69574725ab1aa5153d710eaa651df5e2c35bfeca1ae"},{"bytes":2137038104,"path":"model-00120-of-00120.safetensors","sha256":"15ffa59ac9a36b7f0fbfabdea893dfc6b6db4dd921d7a291b0056062d6937dda"},{"bytes":15576722,"path":"model.safetensors.index.json","sha256":"2f64d21c67c90bbafeb36c4e9b2f06f54063ed439e9f7cf95962d425a1d8515d"},{"bytes":909,"path":"processor_config.json","sha256":"aae38374c94b08cc9b0547c6e64f05b951bd9735cea571c6988f5ed552bed3ed"},{"bytes":361,"path":"provenance/source-model-revision.json","sha256":"5d9aadd98432da86c628866dfd2944084a5b00f208bcbf7de10518994159e699"},{"bytes":413,"path":"quantization/recipe.json","sha256":"22bcfacf0f7e492ecd571f6f19819656ede91b6cfc1c0abb4ae5bf4a0a0df8e5"},{"bytes":37707425,"path":"quantization_config.json","sha256":"1e5cdf56da1929c2a7d8982085324de05bb92144882792f034239e289316c645"},{"bytes":750,"path":"receipts/checkpoint.json","sha256":"efeaed9daed88eb567df5518f1418f0ebbe5c03132a35e70f7506ab90a04efbd"},{"bytes":9447,"path":"runtime/scripts/qualify_glm53_custom_tp2_runtime.py","sha256":"a0e1969f977f926a4477db8fbad174a9ecdfab62c547b051e0e6cd5e3c79bd2a"},{"bytes":3250,"path":"runtime/scripts/run_glm53_custom_tp_runtime.py","sha256":"7c83b2785d338dcaff8a5fe3b87b2957dea6ea8c1c31ddb02465b797f4c4647e"},{"bytes":80,"path":"runtime/src/quant_pipeline/__init__.py","sha256":"c1d7fb3aaa98d2baa24064ad19c91336ecbb89b35e446500e06bff9a1b61045e"},{"bytes":1,"path":"runtime/src/quant_pipeline/allocation/__init__.py","sha256":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b"},{"bytes":5264,"path":"runtime/src/quant_pipeline/allocation/global_dp.py","sha256":"09fa93212c4400f5dba4f47be95a22a52d97c923a0889e2c104e4f54581e0a2e"},{"bytes":2007,"path":"runtime/src/quant_pipeline/calibration/__init__.py","sha256":"b9e11264f116c3ee55d30f4c35080d7e266851b4325742b8e4a58f7e328d5ea7"},{"bytes":94130,"path":"runtime/src/quant_pipeline/calibration/fitter.py","sha256":"038a40c84ee23ab46e8d7e7aefa92fcfe4008c9d0c7204c006481f9c1ce50ba8"},{"bytes":21303,"path":"runtime/src/quant_pipeline/calibration/glm53_capture.py","sha256":"ccb49f16c91b37dcbd235af18de422a7b54773d32f11a3a958172b984fb6c161"},{"bytes":15362,"path":"runtime/src/quant_pipeline/calibration/glm53_mtp_capture.py","sha256":"c91df3cc86363a325a2334291ba6171cf2d5e9cab2d29bb7b09268c0795c2d05"},{"bytes":41337,"path":"runtime/src/quant_pipeline/calibration/qwen_capture.py","sha256":"86a6f7dc01961f6117a880c4dc8ce394af6a31ce821aa32d73ffcf091e2d3a73"},{"bytes":26294,"path":"runtime/src/quant_pipeline/calibration/route_mass.py","sha256":"84f3bc4042b36f9002ab49084ad5629c627ab4e2415651d416b9f96c4e1ca0b4"},{"bytes":6950,"path":"runtime/src/quant_pipeline/calibration/windows.py","sha256":"87cab3cc4dce85458d61e93baac28dde9735335ba2c9b84178675b40d313a08c"},{"bytes":680,"path":"runtime/src/quant_pipeline/campaign/__init__.py","sha256":"6757125714ce3d4e6eb60cb73329d9bb6f64cb20ddf86306b4176a7159c0339f"},{"bytes":49009,"path":"runtime/src/quant_pipeline/campaign/glm53_direct_k4.py","sha256":"de572b6e6b88f00c183e26889fceaf1e94baed151cf3a372a3a5cfc8fbc663de"},{"bytes":23774,"path":"runtime/src/quant_pipeline/campaign/glm53_mcg_preparation.py","sha256":"fc5b456893454b2fa0dc234e3837a7894f48cda3cd96bcabfa22071e54f7552d"},{"bytes":27832,"path":"runtime/src/quant_pipeline/campaign/glm53_mtp_k4.py","sha256":"03bd9f5720a9c19e0dc5026fa78068fbc5fc433056a8a43b3e5e5427ff01627c"},{"bytes":27999,"path":"runtime/src/quant_pipeline/campaign/glm53_prepared_backend.py","sha256":"5b196e0892638f76d1a0a6d8cfacff8f7dcff28d10e1ae0c5636532275b34f9e"},{"bytes":10286,"path":"runtime/src/quant_pipeline/campaign/glm53_provenance.py","sha256":"f93faa0909667e3a71aab46fcc0fde52a7c724d7803cf68e32404ee0f65e29ec"},{"bytes":30486,"path":"runtime/src/quant_pipeline/campaign/glm53_uniform_k4.py","sha256":"a60fbee62ef2faf45b56157c8624a522deb72f6a325450d58c8ab395c4ba7ba5"},{"bytes":6374,"path":"runtime/src/quant_pipeline/campaign/glm_contract.py","sha256":"85d0c3644e380c2ba9017ca199a8caa77b719f4a7df6103e08ba8d3a9db13500"},{"bytes":43115,"path":"runtime/src/quant_pipeline/campaign/qwen_adapter.py","sha256":"1e2d5fed6d8ef3b6f354aa572646df37ba03065d9102cb60dfd07b2a84a5fedc"},{"bytes":41389,"path":"runtime/src/quant_pipeline/campaign/qwen_attribution.py","sha256":"70c0a088047164a98d830271e724a5e2ad36647ffaaccdd80111dc47aaf29854"},{"bytes":93202,"path":"runtime/src/quant_pipeline/campaign/qwen_services.py","sha256":"e8fd5f74f704b2b486bfe85093198c3b049125faafbe4e8f97ca14cea7babc5c"},{"bytes":30808,"path":"runtime/src/quant_pipeline/campaign/qwen_work_units.py","sha256":"a70323b46d86988bd27b5186d4d0103d1b0d0b755e636def7ffd4c7e2cbf2d5e"},{"bytes":107921,"path":"runtime/src/quant_pipeline/campaign/runner.py","sha256":"20e96288b145594e43428a91965f85979ffc7163688d286cd6867ee20dc07ba8"},{"bytes":2733,"path":"runtime/src/quant_pipeline/candidates/__init__.py","sha256":"4c74cc45cdefef59f07265d5996ea4123ba26fe6e69dbc5f54091c0082f47a8b"},{"bytes":14881,"path":"runtime/src/quant_pipeline/candidates/factory_allocation.py","sha256":"96fca4dbe7d85be45052d18c18ed461ad2c2303467c2cbe4b60b425e99813f36"},{"bytes":24214,"path":"runtime/src/quant_pipeline/candidates/factory_calibration.py","sha256":"c5b012837a683106ea21e4e52d5f7853e14eb414f9cc9f37047ebf94e6f9a0c1"},{"bytes":16583,"path":"runtime/src/quant_pipeline/candidates/factory_union.py","sha256":"2d6a1825c23b8149e208db26f302e4c2bf1339e3ec6f51e0784913f15c62b212"},{"bytes":144807,"path":"runtime/src/quant_pipeline/candidates/ledger.py","sha256":"f313d706df03efb5bd7fd06dd1a88ef749170ada6546240bfb02bced07d47162"},{"bytes":5012,"path":"runtime/src/quant_pipeline/candidates/payload_store.py","sha256":"e6450a45cffacc49da22bfdc1eeac70d4a00b884e5525af88a9293b4d46874d6"},{"bytes":385,"path":"runtime/src/quant_pipeline/capacity/__init__.py","sha256":"30366e8c26f27b770fabc6325b833bea97f32087a7e9b48bf0605f5f36ba5a9a"},{"bytes":8588,"path":"runtime/src/quant_pipeline/capacity/mla.py","sha256":"f45ef249c48131005580714888397840400f81e3c6f0caf023382fef1a8ab882"},{"bytes":1100,"path":"runtime/src/quant_pipeline/checkpoint/__init__.py","sha256":"97f1d9bb510eb7be6aeaac52e627ab645de01f3ff2788045087ce0e876ed9cdc"},{"bytes":46413,"path":"runtime/src/quant_pipeline/checkpoint/btx_qwen.py","sha256":"74cf73cf90a2c8d42e071ab10da343b3e4ae3666c848703287b53706d91adbcb"},{"bytes":10040,"path":"runtime/src/quant_pipeline/checkpoint/exact_payload.py","sha256":"65fd985494625d2b1e0541ae9106d57a085063f22df1cce2bf0307b63970e47b"},{"bytes":37041,"path":"runtime/src/quant_pipeline/checkpoint/glm53_mcg_materializer.py","sha256":"a9aad8da1d228afaf2edd2cd84a6657ac5b53628554e89918484aca20d602d22"},{"bytes":31800,"path":"runtime/src/quant_pipeline/checkpoint/official_btx.py","sha256":"19466f3fd531a03f7c43fabafe2ca395981d47cdd1ed818630a5fb80213817b6"},{"bytes":10456,"path":"runtime/src/quant_pipeline/checkpoint/packed_payload.py","sha256":"36f84586d89ae7816172929471a35dc81ec5beff63c47a68cca862b61a1dc4dc"},{"bytes":11585,"path":"runtime/src/quant_pipeline/checkpoint/reference_pack.py","sha256":"4a00fffddd2993998af7c3c660efbfdc4ea0abff96a17d60db969f193b3a60cb"},{"bytes":16525,"path":"runtime/src/quant_pipeline/cli.py","sha256":"d75754a774f21054d221b76ca82863da1fc343d409107eb925cf64ec67456fb9"},{"bytes":1,"path":"runtime/src/quant_pipeline/codecs/__init__.py","sha256":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b"},{"bytes":8458,"path":"runtime/src/quant_pipeline/codecs/exl3_mcg.py","sha256":"13263a584125d0600a580eb63ba7397d4abcb70d8a0be4a3fb328c9ec53741cd"},{"bytes":644,"path":"runtime/src/quant_pipeline/codecs/protocols.py","sha256":"5615adfb8e39407a18b6ad6e4942985e14483eae7b4604bf6b85111441e1e652"},{"bytes":2973,"path":"runtime/src/quant_pipeline/codecs/uniform.py","sha256":"a1910785f0bab4f498563f135984f55c68735869203fd8d076ffcd9ced67c5ed"},{"bytes":1,"path":"runtime/src/quant_pipeline/core/__init__.py","sha256":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b"},{"bytes":3014,"path":"runtime/src/quant_pipeline/core/artifacts.py","sha256":"b50023ac37b2c1bf78a1497e243ad8dab11330a45b12c2fd1d99af17470e2087"},{"bytes":66,"path":"runtime/src/quant_pipeline/evaluation/__init__.py","sha256":"a85ccafbfb64a7bb4dafd254966fcc5864056b6ed60dd68acee6b6a91a8092ed"},{"bytes":8849,"path":"runtime/src/quant_pipeline/evaluation/glm53_logits.py","sha256":"22d0bb24bd350cfe45f701a5f6e57bd0e6f17666ae8b7538abfc54b1d4845a3d"},{"bytes":25340,"path":"runtime/src/quant_pipeline/evaluation/glm53_packed_k4_reader.py","sha256":"994b211f75411a5b8a0f9242559adfa062c28284e5cf8b215df8dedef8e0583d"},{"bytes":6536,"path":"runtime/src/quant_pipeline/evaluation/kld_window.py","sha256":"dbe98e31e77e6feb60c95b8f1eaa2825328b28515be6add492fc88e5fa6b9567"},{"bytes":1,"path":"runtime/src/quant_pipeline/models/__init__.py","sha256":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b"},{"bytes":8693,"path":"runtime/src/quant_pipeline/models/hf_capture.py","sha256":"72f151f612f4ed5d4de7f1dd4b2a0f63c3a787ad7d6a0a966cfbc3524cc20525"},{"bytes":10534,"path":"runtime/src/quant_pipeline/models/inventory.py","sha256":"c2f8344e2fe62de4a4b95b9e27866877005e268894ea52ca6218cb0ca16a7300"},{"bytes":2529,"path":"runtime/src/quant_pipeline/normalization/__init__.py","sha256":"8611f5f6678017e1e32f934ae659106eeed5ac376236450156616a0d56366a96"},{"bytes":22128,"path":"runtime/src/quant_pipeline/normalization/absolute_v31.py","sha256":"eb3dd0ea0a59e16085ffe116d692af6de84f0d6bb8a5482a69290a175c44ea57"},{"bytes":29068,"path":"runtime/src/quant_pipeline/normalization/artifact_v31.py","sha256":"d257d5f5e04ed7c2ec6341050c12015d0038428381d807505966139ba00276bf"},{"bytes":9990,"path":"runtime/src/quant_pipeline/normalization/prior_search.py","sha256":"d883764ff24fd2de0e7809bde20e3f33d0c8aa1122929b37e4b843912a85a1d5"},{"bytes":25990,"path":"runtime/src/quant_pipeline/normalization/streaming_v31.py","sha256":"573ded7f185316ecaf5a581f05b102b56d546e20bc1fd5a4dc73feae9a951da2"},{"bytes":56,"path":"runtime/src/quant_pipeline/publication/__init__.py","sha256":"fdf134e41cc7ab16d894259271eab7149131fa7ffdac7676f73c18b6edf6dac3"},{"bytes":17931,"path":"runtime/src/quant_pipeline/publication/glm53_hf.py","sha256":"bc561815be7a34e3f482aa372cc7e6600c3b368813e3fcfac42c400f8b36ac23"},{"bytes":15663,"path":"runtime/src/quant_pipeline/publication/glm53_k4_postmtp.py","sha256":"49f9917cb9b88fbeab1d6b88ab75b9d830a4a563d8f9f89490a2e9efc5bfecc2"},{"bytes":357,"path":"runtime/src/quant_pipeline/results/__init__.py","sha256":"0bc92706bc208a322b806de849e57b8ac7c39d3c90ed86934de6e69414b0ac07"},{"bytes":12980,"path":"runtime/src/quant_pipeline/results/ledger.py","sha256":"44fb83ded80dca0e115d9285b2143053339bbc4231f9fd112c739898637bc696"},{"bytes":459,"path":"runtime/src/quant_pipeline/runtime/__init__.py","sha256":"c44558f4859c2111808ab02d654214ce02a1a8d7559030cb7d684700d76a50c4"},{"bytes":43930,"path":"runtime/src/quant_pipeline/runtime/glm53_tp2_exl3.py","sha256":"11617266954541f4c7337bf90501ec6f540ae3a346c486d90bdbe3380d625e67"},{"bytes":1,"path":"runtime/src/quant_pipeline/scoring/__init__.py","sha256":"01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b"},{"bytes":10444,"path":"runtime/src/quant_pipeline/scoring/attribution.py","sha256":"ff386c0463d135b1b6e3c67f5daa0c16847b206253727edd6a453386ae1d4be3"},{"bytes":2779,"path":"runtime/src/quant_pipeline/scoring/blend.py","sha256":"8beb3e84f9f11263bcc2b69bc122cdf09133289cdc992a6d75cef7c4137c5859"},{"bytes":2797,"path":"runtime/src/quant_pipeline/scoring/kld.py","sha256":"a43789dc10a504c8f3b13a9e760e323e443e95c551579a54b6fa1c210ffe0d3b"},{"bytes":4103,"path":"runtime/src/quant_pipeline/scoring/qwen_experts.py","sha256":"fc2787667aef44bf1705b1ef4bb7bae984782c92ac69ff314ab8bf60f471f80f"},{"bytes":3095,"path":"runtime/src/quant_pipeline/spec.py","sha256":"9aed1d7b7aa54f1d65ad01a61d1b50de20fb3c6ec90644a25e49e317628707fd"},{"bytes":20217442,"path":"tokenizer.json","sha256":"19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d"},{"bytes":761,"path":"tokenizer_config.json","sha256":"98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc"}],"label":"glm53-shapleymcg-uniform-k4-model","manifest_sha256":"8c1228294a693f7b159729d86067254ab2e5f8eeb6cec9c9a271c824ee3ec99b","schema":"quant-pipeline.artifact-tree-manifest.v1","total_bytes":175779720303}
|
README.md
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# GLM-5.3-Flash-EXL3-4bpw
|
| 2 |
+
|
| 3 |
+
Source: `zai-org/GLM-5.3-Flash-BF16@a6c167b62691b2bac901344b65cb651a70f53e43`. All routed experts including MTP45 are uniform four-bit EXL3/TR3 MCG; non-routed tensors retain their official native dtype. The custom TP2 runtime is qualified by actual-runtime BF16-teacher KLD plus byte-identical rank outputs and multi-token generation; its stricter decoded raw-logit parity diagnostic remains failed.
|
| 4 |
+
|
| 5 |
+
Five-cold-run mean teacher-to-student KLD: `0.024554564250` over 51,175 sealed causal positions per run. Actual TP2 runtime qualification-window KLD: `0.022750847878` over 2,047 positions (both gates: mean KLD < 0.06). This checkpoint requires the included custom Transformers TP2 adapter and is not a stock vLLM/ExLlamaV3 compatibility claim.
|
| 6 |
+
|
| 7 |
+
## Five cold KLD runs
|
| 8 |
+
|
| 9 |
+
| Run | Mean teacher-to-student KLD | Positions | Report receipt | Capture receipt |
|
| 10 |
+
|---:|---:|---:|---|---|
|
| 11 |
+
| 1 | 0.024554564249958 | 51,175 | `ef6a8dedc20f11e582658f94923da3e66c2b6cea4ff62d936abb790e376e2461` | `013759025d8414f8811fa140250e2c79097c1082926edd4ae2cfc6751722fc8d` |
|
| 12 |
+
| 2 | 0.024554564249958 | 51,175 | `b7d1cac829f6b21471da4ea724aac479f9db250d4286edd412e099fa747f8257` | `eae08903737bde9f31bf6f8632d2de7b6539f4b1efd8113c5f81461d92aaf671` |
|
| 13 |
+
| 3 | 0.024554564249958 | 51,175 | `663629ccd2bda08a4c299d767b7e6e6d622a81ad6830ad1acf08d0eb8ca1a196` | `000896721ea7116322eb31d8e75718985d29240fabd6a921627bb02c03516bec` |
|
| 14 |
+
| 4 | 0.024554564249958 | 51,175 | `cdb2d8ee4ce795f695f335f0bb3ce7bd135dcf6df4f48c6e3862b40cd1340586` | `7ece4defa651c3693bffd624ad7d07ff85c0dceb7674a7752ec136dea6370c3f` |
|
| 15 |
+
| 5 | 0.024554564249958 | 51,175 | `ac4d6d94aef27b09ca9b2dd513516e793cf5f4afe3d1f2b008a3fb4ed64ae243` | `5b59145332206b4c0fb82f791e2c09be8fadb16d18e6e58818b78e919294cb65` |
|
| 16 |
+
|
| 17 |
+
All five accepted executions used the same sealed 25-window panel, so each has 51,175 causal prediction positions. They produced the same tokenwise-KLD SHA-256 and a population standard deviation of zero. The first attempt at the fifth capture received an external SIGTERM before it wrote any logits; it retained only its plan and reader identity and is excluded. The table's run 5 is the clean `run5b` retry, with a distinct cold-execution backend/capture receipt and the same measured KLD as runs 1-4.
|
| 18 |
+
|
| 19 |
+
The direct packed TP2 serving result (`0.022750847878`) is a separate one-window runtime qualification measurement, not a replacement for the five full-panel runs. The raw decoded-logit absolute-error diagnostic remains failed and is disclosed in the receipts; qualification is based on teacher-to-runtime KLD, rank-identical output, complete packed-tensor census, and multi-token generation.
|
| 20 |
+
|
| 21 |
+
Code and the five-run receipts: [brandonmmusic-max/glm-5.3-flash-exl3-4bpw](https://github.com/brandonmmusic-max/glm-5.3-flash-exl3-4bpw).
|
| 22 |
+
|
| 23 |
+
## Minimal TP2 launch
|
| 24 |
+
|
| 25 |
+
Use Transformers 5.16.1, clone ExLlamaV3 at commit `c5d9c657966ffeeaa9353f0cc899f18629da4a13`, compile its CUDA extension, then run:
|
| 26 |
+
|
| 27 |
+
```bash
|
| 28 |
+
PYTHONPATH=runtime/src torchrun --standalone --nproc-per-node=2 runtime/scripts/run_glm53_custom_tp_runtime.py --model . --exllamav3-source /path/to/exllamav3 --prompt 'Hello'
|
| 29 |
+
```
|
SHA256SUMS
ADDED
|
@@ -0,0 +1,328 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930 .gitattributes
|
| 2 |
+
8d0185b871ad9cd46bfd2495a01675046b306a8510a62cea36aaa3e3cbf51f1f .materialization/shards/model-00001-of-00120.safetensors.json
|
| 3 |
+
077b2416ef12b929a278b1b86935159ab35f88ac2306f5be7e36e5d07b901a76 .materialization/shards/model-00002-of-00120.safetensors.json
|
| 4 |
+
c2103a15e275cc17e3217299638a877bbc236839d258ee6837fb4fc491d3a88e .materialization/shards/model-00003-of-00120.safetensors.json
|
| 5 |
+
73d7098787cb673ee5d898033f796e0e2ad4e80ec19ddc1bb4a7d7a89d7c8858 .materialization/shards/model-00004-of-00120.safetensors.json
|
| 6 |
+
8cee876d2e1e783efe86a3b130e3c3b8e7fd7df4e01e82586c454151da882c7c .materialization/shards/model-00005-of-00120.safetensors.json
|
| 7 |
+
6b293a767b22b48b4d80472891b5e498cb8e89c32a9a4d029d38fc4652e86dbe .materialization/shards/model-00006-of-00120.safetensors.json
|
| 8 |
+
47c858836e8ab1a6e495e150c0b0c63f315454c7a796e45d385c75e021b9d192 .materialization/shards/model-00007-of-00120.safetensors.json
|
| 9 |
+
921d814d6fa5d94be30a74b4d89543ec936c781ca926d18dcb20957bfdf57260 .materialization/shards/model-00008-of-00120.safetensors.json
|
| 10 |
+
a22c769d3ba85523734d01dfcbb5cc665aa92ee03544167b6d74efae28546391 .materialization/shards/model-00009-of-00120.safetensors.json
|
| 11 |
+
99f73edddd6b5156af945d26dd08cd48ab8fe313cefdc5d4e68aa06a89808864 .materialization/shards/model-00010-of-00120.safetensors.json
|
| 12 |
+
8429044dd8cb7908b21ed9b90e107e2f1a8590d504e6600426911ac699f98ea6 .materialization/shards/model-00011-of-00120.safetensors.json
|
| 13 |
+
fa143b20a1ea05fcc5628a1e930568c8011f5dd1511b46b5e640d511d5ae6162 .materialization/shards/model-00012-of-00120.safetensors.json
|
| 14 |
+
9b9641a47add516930a62bf976d0d7492d974d82b851945b56fecb92e1f8df85 .materialization/shards/model-00013-of-00120.safetensors.json
|
| 15 |
+
de0f74e000942557041bc0bcc74f72539f472d58827bbc6aeb8797b8e663abed .materialization/shards/model-00014-of-00120.safetensors.json
|
| 16 |
+
0ef95d29638dd3035030d6b490eebd22b9daa83027377551f4681f540a9e0f81 .materialization/shards/model-00015-of-00120.safetensors.json
|
| 17 |
+
8677b1c192cb18073f84537ba0c3ae233620468e946679ff9e73db9f83514c0b .materialization/shards/model-00016-of-00120.safetensors.json
|
| 18 |
+
aa9d28fb398294ff354dbf2d914110789f459ad245d851ee3ec377f208327cd2 .materialization/shards/model-00017-of-00120.safetensors.json
|
| 19 |
+
90fe4172bce89fd958b0e85868ff6d25b3bc1a811d5d6ba79ad7181c93e169ef .materialization/shards/model-00018-of-00120.safetensors.json
|
| 20 |
+
7f77f080f0190dd73a3c74869fc8a8b4622e967afea7fe8964f42b28d775146e .materialization/shards/model-00019-of-00120.safetensors.json
|
| 21 |
+
db733326f671217c5cad427e78f32ae5aa68f55267fe8c261927cb8c2ea31df7 .materialization/shards/model-00020-of-00120.safetensors.json
|
| 22 |
+
c09133ff8c45bf88f5cc85c3d6ef6bd29b10c522e71948849ade3ced2aba0429 .materialization/shards/model-00021-of-00120.safetensors.json
|
| 23 |
+
07416400c38b38ca3971e96f521700f45de531868d4ea4129aee6fdd037dc32a .materialization/shards/model-00022-of-00120.safetensors.json
|
| 24 |
+
a4543fc7b6e65354b79087008cb0559cf089bbf9c656c88461a2ced2ef5e669d .materialization/shards/model-00023-of-00120.safetensors.json
|
| 25 |
+
d784dcad595674f61f6f2e5368d193f20a0bebaf0d8b8b66d08d5ea0c5f68891 .materialization/shards/model-00024-of-00120.safetensors.json
|
| 26 |
+
739e95aef584be373fdf1cc5392062f55bfd6a51c56b0a0eed8f99601263cbbc .materialization/shards/model-00025-of-00120.safetensors.json
|
| 27 |
+
cc822a4141b4fb1259cb7cf0019bd2767328a648055d93c3ac2d85ac65703614 .materialization/shards/model-00026-of-00120.safetensors.json
|
| 28 |
+
07009dae0fc9abf3c98eeedde578aca609a3d736a00dd1f66704fe5952748353 .materialization/shards/model-00027-of-00120.safetensors.json
|
| 29 |
+
ba4864837bf40411112c01fd7c5baf58e1a9772a39f8ef4b0b4ced269f964e46 .materialization/shards/model-00028-of-00120.safetensors.json
|
| 30 |
+
c8bb1835ddd126ba689ac711a8ae6d91a2ac6289e026c2e9f50db148f6220dd6 .materialization/shards/model-00029-of-00120.safetensors.json
|
| 31 |
+
763056215e677578fcd4b7eb2aafe7e20df03fc33abbe0fde1cb3d799d9aae3d .materialization/shards/model-00030-of-00120.safetensors.json
|
| 32 |
+
0cf92f1415301a92d63dfacb26d58ebafe44f94609fe065fbc51be79280a621d .materialization/shards/model-00031-of-00120.safetensors.json
|
| 33 |
+
a15212287887e7610f83a2a073b6291888bc70fc95a34d02ce2c3045643d9a13 .materialization/shards/model-00032-of-00120.safetensors.json
|
| 34 |
+
d882b1d207bd3e456a679df22ae1910df3b4c8db73a2cce28e25e40517d99d4a .materialization/shards/model-00033-of-00120.safetensors.json
|
| 35 |
+
2d0abb1c9b502f3ad008244118fe45f761640231952b4ccacc6e0f5c01c05c55 .materialization/shards/model-00034-of-00120.safetensors.json
|
| 36 |
+
eb424f041ac17b7cceb62e6c98418cd5bbeb62b52c71ddc2a19583640f5d2f62 .materialization/shards/model-00035-of-00120.safetensors.json
|
| 37 |
+
81b1e3b999e8fed06b7308c08b17d71d46fcadbf41fba2a6507769102e4b1bbf .materialization/shards/model-00036-of-00120.safetensors.json
|
| 38 |
+
604d281b96a22329c12501ba093d1588b82053cf259d7f1ce593025eba8fe534 .materialization/shards/model-00037-of-00120.safetensors.json
|
| 39 |
+
2b0087db11b5350b33dd5a6cd7858dedc6f14e01748f9559308c48b66cd80cc9 .materialization/shards/model-00038-of-00120.safetensors.json
|
| 40 |
+
6a783143193bf10e39118c68ad4e17744de2b005f1a14dc2fb20accc08fc841b .materialization/shards/model-00039-of-00120.safetensors.json
|
| 41 |
+
9e89153f0dece501d3e4a2d2f9a513bd2c782e15a925cd893cd68393d255a14b .materialization/shards/model-00040-of-00120.safetensors.json
|
| 42 |
+
ea9abf170718fcaf85858530d184f61eab6568938bb4958c0a2be1d7906782a6 .materialization/shards/model-00041-of-00120.safetensors.json
|
| 43 |
+
c401ff0d4ca1deb7fd58f324848f35c5b7ca3d2eab99a96710c3febbfa7ffa20 .materialization/shards/model-00042-of-00120.safetensors.json
|
| 44 |
+
c70d2d8c4c80486569f91e0b33d182249b038400d5ab03e7e2875f65dc7b5dd8 .materialization/shards/model-00043-of-00120.safetensors.json
|
| 45 |
+
6866929f97d6acf531ce8a92d08c5f5e59dc0c5913cfb31aebf77774318bb86f .materialization/shards/model-00044-of-00120.safetensors.json
|
| 46 |
+
951c0e78d38c301fc1b4d1387d075b1fda33ab1551defda7052803a8291bf727 .materialization/shards/model-00045-of-00120.safetensors.json
|
| 47 |
+
2ed00e3532d42a5c41cdf74f912dc4152636c399798774136524ce0592454f23 .materialization/shards/model-00046-of-00120.safetensors.json
|
| 48 |
+
b67b04238eaf12d7b7ec03642c9bb20df835668bde3f4b4477464b457db2bbf6 .materialization/shards/model-00047-of-00120.safetensors.json
|
| 49 |
+
ec608572e29733b6a6027f61cbe84781cdef2c701ca2eea81fe482f1b937b77f .materialization/shards/model-00048-of-00120.safetensors.json
|
| 50 |
+
79cbe9a141d8400912c1d4c7f7d58857e67c37e9f384af71ed15963e38ff8e2d .materialization/shards/model-00049-of-00120.safetensors.json
|
| 51 |
+
d4c415f20d313493560175e2aff6bf84ea5e927d5d0ba7e7a672508560b9b90e .materialization/shards/model-00050-of-00120.safetensors.json
|
| 52 |
+
f4c3aef067188c331a71882a5f099e8e397029a260c8c4a85234434b4320acf0 .materialization/shards/model-00051-of-00120.safetensors.json
|
| 53 |
+
1358ae41eaa3fa5620f35cb35629e3f429e8dc9af182b17e7f9a14dc53a6333d .materialization/shards/model-00052-of-00120.safetensors.json
|
| 54 |
+
b0353f47312da53cd094feaaf187e411ffc228f19af512b2b96431dfbcd0cc30 .materialization/shards/model-00053-of-00120.safetensors.json
|
| 55 |
+
2f776186445cb810611b4e447257b8af7ccfd0a6580a5d16dc7055650dcbff0d .materialization/shards/model-00054-of-00120.safetensors.json
|
| 56 |
+
fd3cbdf6b316ea46c54a52042d5d5b0547061ef08e7457a7f421a5d4c7bed0c0 .materialization/shards/model-00055-of-00120.safetensors.json
|
| 57 |
+
f4f824e1f264c65acfd0c5c3bc0e12400b836c0214aacbb5b7846857c3fdce2a .materialization/shards/model-00056-of-00120.safetensors.json
|
| 58 |
+
410e062323aaee86563d85cd1f79c6729c1b5431acd8891ba7bc851ee4ec468f .materialization/shards/model-00057-of-00120.safetensors.json
|
| 59 |
+
55588c9e6ae8303db9def20956edf71be1f38347f3db293319c1ac5b381a4667 .materialization/shards/model-00058-of-00120.safetensors.json
|
| 60 |
+
83b87d82fa6a66a9d851f97dd97af69f8cdbc4fdc711bb390d68b0ef9062de9a .materialization/shards/model-00059-of-00120.safetensors.json
|
| 61 |
+
f5c286aa7c6274412a8a51412b17fe3ebe8c9e2e751fe17ea61854320b986dc9 .materialization/shards/model-00060-of-00120.safetensors.json
|
| 62 |
+
d4c780a921de809dddb8ec92b6d9b7396c139ee6aa15822bc232edd21b7b0751 .materialization/shards/model-00061-of-00120.safetensors.json
|
| 63 |
+
d28dca2459ae348608e5a2ad1582add754a6135e03a29f6a66cd529abc3335cd .materialization/shards/model-00062-of-00120.safetensors.json
|
| 64 |
+
3ce37e9b9c75627ec0046b805cf7bc166a028fc9cd8f4ae27870e32a19ed347a .materialization/shards/model-00063-of-00120.safetensors.json
|
| 65 |
+
a2e7b7ce8aedea8c7f1b79b0d4eb8aadc62f935ef544ebe961e3bf22ddb745ad .materialization/shards/model-00064-of-00120.safetensors.json
|
| 66 |
+
50e8cebcdac5f20d2626b6eb4f65a45db2fc2da0b196d09122b8b3bafc71bc4d .materialization/shards/model-00065-of-00120.safetensors.json
|
| 67 |
+
db66bcfc88353450e4e76689a86036d1171c06eb0b153f26598ba0ba36fd148a .materialization/shards/model-00066-of-00120.safetensors.json
|
| 68 |
+
99ca7773e9e74fe1da2d149eddf3060c14df4a22d4b0947b6dfc6ada1072c022 .materialization/shards/model-00067-of-00120.safetensors.json
|
| 69 |
+
3981caef5a222ff73a55a306895252f8418dc2560fc53ccb1d8816c2f95abbe5 .materialization/shards/model-00068-of-00120.safetensors.json
|
| 70 |
+
647307884644c56ea8e95e66cb7bb478e89df2247a058aadbe6528fd4e8a1627 .materialization/shards/model-00069-of-00120.safetensors.json
|
| 71 |
+
61fc4536e257ed876df79998c4468c5d97b7927b13ba31d9567202cd74f92e4c .materialization/shards/model-00070-of-00120.safetensors.json
|
| 72 |
+
cf6dba6de255e800f25de85b31f4b0df5f09710b187e5b3b499d97caa032497d .materialization/shards/model-00071-of-00120.safetensors.json
|
| 73 |
+
edb9724f88e0a18f495e74c72ecf8a801dc8093bef7dca7797ea98de3d8ae32e .materialization/shards/model-00072-of-00120.safetensors.json
|
| 74 |
+
3d397578ba4920e9427939afaabdac25b80162270fbed341a9d600889063ba94 .materialization/shards/model-00073-of-00120.safetensors.json
|
| 75 |
+
161d74aa4d598927b4bd23b952a8a8c17ef0a1ebdacd6a4120f0947e837dfef5 .materialization/shards/model-00074-of-00120.safetensors.json
|
| 76 |
+
5f1876dbf3556d818d988c49b9ed6e22b93a5c249865d48a6d7d002aad54e2ff .materialization/shards/model-00075-of-00120.safetensors.json
|
| 77 |
+
bf46b5a32e3361922ec127ae2ba06469056d9f610f0f135e0c47e5f7f9ca0314 .materialization/shards/model-00076-of-00120.safetensors.json
|
| 78 |
+
1ffb769cb74f6e848a90977b96b5fcec66e00e8ba23f5c1a4fe442288eef2493 .materialization/shards/model-00077-of-00120.safetensors.json
|
| 79 |
+
ff215ea0a4fa6230abf2712ec63f17c6f672dded395bb3531d8c211abf5a4304 .materialization/shards/model-00078-of-00120.safetensors.json
|
| 80 |
+
35c16d1ff4827312c2cd40e435bddc03a803e9b1a08d898987e3d9e673423715 .materialization/shards/model-00079-of-00120.safetensors.json
|
| 81 |
+
1500c89f765f2807c52c408b0bd72826d50c6eb2cec030521b3325430bd0ad2b .materialization/shards/model-00080-of-00120.safetensors.json
|
| 82 |
+
524b750e2684840aa95e06f7fed8aab4a6908cb019ef6cf010b764b2b0458102 .materialization/shards/model-00081-of-00120.safetensors.json
|
| 83 |
+
3febf8cefa4f8a0823ce773190435a80863bbd721a7dcac20b74644b4b18618e .materialization/shards/model-00082-of-00120.safetensors.json
|
| 84 |
+
bbf37f5ddb8490fd85ddf3df51a0c0ced34b928464bb9b10a271cc335c6b83d6 .materialization/shards/model-00083-of-00120.safetensors.json
|
| 85 |
+
0f9981fa883c348642c735d87809908ad7eccad414c0deae29f6fa6e3dc8c638 .materialization/shards/model-00084-of-00120.safetensors.json
|
| 86 |
+
cefe465f7910a51bd80efa3ece377dd6d75b8b3f6a694350160ef207da458cf6 .materialization/shards/model-00085-of-00120.safetensors.json
|
| 87 |
+
c0949f39be57d6de4ca1ea4018fd6bfec22f388f3a66bca809582f845b790668 .materialization/shards/model-00086-of-00120.safetensors.json
|
| 88 |
+
a22a40e918551b7e1ebb4e3bca3cd96c79d24aa76906b40d7a819e96b1eea58c .materialization/shards/model-00087-of-00120.safetensors.json
|
| 89 |
+
ad009d666b4fa9715614d90261b210e9fa97dc96a12efefec36c9519c818aeac .materialization/shards/model-00088-of-00120.safetensors.json
|
| 90 |
+
068da6054f6be308f6f6fa97239a7f7fea46a49d4bb371970a5d2356555c60fb .materialization/shards/model-00089-of-00120.safetensors.json
|
| 91 |
+
a1b3e5529b0e1474ee6375c536104fe5f72a19ea6a59960ef6a33cdea5beab8f .materialization/shards/model-00090-of-00120.safetensors.json
|
| 92 |
+
3c7f9a4ac82c180b29d8c25ae35a51df8767f515dcac9c67dd782a249e697108 .materialization/shards/model-00091-of-00120.safetensors.json
|
| 93 |
+
33d7d5fe815f600493bde03df7442d7b2cf1f4d39ad26e01c6f7ea97332181b1 .materialization/shards/model-00092-of-00120.safetensors.json
|
| 94 |
+
36fd776f87fddafda48ef9a99dd65f0c7beb0fb09eac8a33babd33fa423f5c59 .materialization/shards/model-00093-of-00120.safetensors.json
|
| 95 |
+
405cb805af678eeeddc4423f5cf9449a3ab3a445ae818a649180b0b25cdfd165 .materialization/shards/model-00094-of-00120.safetensors.json
|
| 96 |
+
2e6283cb01dcaf4030d1ff3ce4f25d94fe0bb648783ac71cc51b4b6d2590322e .materialization/shards/model-00095-of-00120.safetensors.json
|
| 97 |
+
73828ad22dd3011d9df1f0d52f3d0c8561e7a4d45ca86e558551b8d9f8f00fe2 .materialization/shards/model-00096-of-00120.safetensors.json
|
| 98 |
+
a2d06e799b3f03f35c8566696d08f3fc8a1572de9de51a046754adc05e3bfa44 .materialization/shards/model-00097-of-00120.safetensors.json
|
| 99 |
+
71ca94c8d0d7df0d1eab2a1d1e9a45ca251c18abdc3f6b3c746374bf8aa644dc .materialization/shards/model-00098-of-00120.safetensors.json
|
| 100 |
+
2a02fb2af3083610f3fbe051a641a3c8c4fb80e6d0e4ce24331bff1612924616 .materialization/shards/model-00099-of-00120.safetensors.json
|
| 101 |
+
f5448b63555d18468904a19591ceaee14f4885f92c29cc8b7d74bbfee66353cb .materialization/shards/model-00100-of-00120.safetensors.json
|
| 102 |
+
f99a0c865757ceb105d64d44724bc997d818978e633b5f89f42cc53ee39a3348 .materialization/shards/model-00101-of-00120.safetensors.json
|
| 103 |
+
36fecc968b5d60c7e9d7a26b0c3acb0c7829c7150eb1a08403e7db2e0987b166 .materialization/shards/model-00102-of-00120.safetensors.json
|
| 104 |
+
7a739335a53fc49fe54371e9aa9d8d40df8d746cbd9548f11ca4cce92de2496c .materialization/shards/model-00103-of-00120.safetensors.json
|
| 105 |
+
eefb364739fb126393e649631845bba544c71b24bd5f220e198149ab8b4760a4 .materialization/shards/model-00104-of-00120.safetensors.json
|
| 106 |
+
67cac41752379d844efd1fcdec4ea8e4c302e241302660727e24827fd2964365 .materialization/shards/model-00105-of-00120.safetensors.json
|
| 107 |
+
57734229ac3b02140e513d0bfa81723b25d8bb17856f45a6833dee00ab6bec35 .materialization/shards/model-00106-of-00120.safetensors.json
|
| 108 |
+
7e44434bae18c4bab9e41760c5b4e784430c153162b4c2c13deab19df629774a .materialization/shards/model-00107-of-00120.safetensors.json
|
| 109 |
+
5f561d9d726b3b114088ead3a8a4a069399d037bfab5ae9c38a0db06089f7c96 .materialization/shards/model-00108-of-00120.safetensors.json
|
| 110 |
+
ea22c6234feb136188ee0c31d7a3ec5bc12944940da201ed3d11234b91805dd5 .materialization/shards/model-00109-of-00120.safetensors.json
|
| 111 |
+
45f4f539f8d355d7b1d4362cd0e95d321c09aa3aba7b764dc453a49e106b1927 .materialization/shards/model-00110-of-00120.safetensors.json
|
| 112 |
+
f96e849ada435b0b92876f36d923417fff687509a0bb4accdc235899c480bbe2 .materialization/shards/model-00111-of-00120.safetensors.json
|
| 113 |
+
4a5064c1a21905ac00b00a48e7fe5bec3fad496beaec9e9491992a4b690e4fe8 .materialization/shards/model-00112-of-00120.safetensors.json
|
| 114 |
+
2abe6623a705200afca4e58c08c6f9a067fef20d174075783a3581b81e03c6e7 .materialization/shards/model-00113-of-00120.safetensors.json
|
| 115 |
+
f792a91136ab2bd70bad63331adcda728fb3a2eb71dfd886d16aae7d38b2b5f0 .materialization/shards/model-00114-of-00120.safetensors.json
|
| 116 |
+
970721ee8cf97f5d2fa1d66f1aa0eec1011cdfb83c60851824126fd7e2abb84a .materialization/shards/model-00115-of-00120.safetensors.json
|
| 117 |
+
9844a9640265951c0817114d290380acaf92a9a7f2dcaa124800162e2fe38dc0 .materialization/shards/model-00116-of-00120.safetensors.json
|
| 118 |
+
3e29504486877265861ffb6e5653984c9ed21eb21a2f0b0b1d777cfb9503b799 .materialization/shards/model-00117-of-00120.safetensors.json
|
| 119 |
+
8be4ecc40a57e7a2c59f444d4621c64548eddd6314aca3dcabf20ad2891cb88d .materialization/shards/model-00118-of-00120.safetensors.json
|
| 120 |
+
e16b7191616ff1effa78859b5ea9e5db9e9f516d13292a37a4850848f757e2fd .materialization/shards/model-00119-of-00120.safetensors.json
|
| 121 |
+
14e78d614464c3395ed1810b5c05d553cb3031b3ae77269babb787936a80d613 .materialization/shards/model-00120-of-00120.safetensors.json
|
| 122 |
+
30b85b6b9659f2e78aa259f8faf5d920a68dee7c9ced3fa6dba1f19f2bc4fca1 LICENSE
|
| 123 |
+
a24ed04666047362a7ca6b8798453996631e977633f2e3b10b5ad1aaea815750 README.md
|
| 124 |
+
41cff9af7b3a86c96751b107a8444f245fbda0bd5320b636a5bb1f7f4ba1a5c3 chat_template.jinja
|
| 125 |
+
4f5341e048984459471bfb9c894e6bf87e69b9c67402672af901631d1349f265 config.json
|
| 126 |
+
c51ff4803f8d0bc69b2ef57ec2f5d72129a5e28e0161221ffd2977eba35a0e69 exl3-mcg-storage-abi.json
|
| 127 |
+
230c30609ecbbb9e6583bedde8e7bdda0c6eb8fe5fad0eaeb3d1b293d751cb4f generation_config.json
|
| 128 |
+
afe588284702c0676b7af5df48bc0e0568bb42b1821808ab71c6dfa1f0c48b61 materialization-receipt.json
|
| 129 |
+
46cc9e99897500fee92f2f08c95665cda51a704f835548465a1023cd4db63ef7 model-00001-of-00120.safetensors
|
| 130 |
+
b78eebdbee138150d310d7c655baae1ea31720b8ad53f39389b1679ce0f51552 model-00002-of-00120.safetensors
|
| 131 |
+
d377c4b645e7283f21370a1d12d90f238ec38194f78ddc483aea6be45c0db2c6 model-00003-of-00120.safetensors
|
| 132 |
+
65f841ba0f3449b18af49eca276db6047a78088a3ff293f9edd41dcb4082af65 model-00004-of-00120.safetensors
|
| 133 |
+
d0425becf2a42244527553a983b269e2656521069c6e9467d0ce6912dd2fee80 model-00005-of-00120.safetensors
|
| 134 |
+
3f6ded63fe3fe96a176c1ffad431c2e7350307238a89fb89ac3947605ea4d72a model-00006-of-00120.safetensors
|
| 135 |
+
a8e2cf2d869afb825934ad43bcbbf7e16132bdfca8443ca52a39c46b36f775cd model-00007-of-00120.safetensors
|
| 136 |
+
eaaf3e209d6bcd37efae31e10fbbc822755629a701076ad9a527d80c216810d5 model-00008-of-00120.safetensors
|
| 137 |
+
ad30bcac854cc303563eb2f280439bf86d7b56c81a43e00175fecd55d63f9905 model-00009-of-00120.safetensors
|
| 138 |
+
35530edfbce782687f3b06b85f85f34decc537a53015c0bee1a4caf82c7cc78b model-00010-of-00120.safetensors
|
| 139 |
+
686f13c8151614b4d85c02c20e8a786bdfa1eac82d8260bd8d877833db346288 model-00011-of-00120.safetensors
|
| 140 |
+
79a19881d8a37ac0afbcd0b0e77ea45150900467b43ae0b412288ce82154a718 model-00012-of-00120.safetensors
|
| 141 |
+
d07940d68fc7b90f99eca406819e4bcd94995a909e32f0f3d15f3251c8f06552 model-00013-of-00120.safetensors
|
| 142 |
+
ca3290268d136656a2b8eb56328fcf275824bcd60c269a39d9b1ee02d29d42fd model-00014-of-00120.safetensors
|
| 143 |
+
2172d6d1c76e4d79d0d6147e693080a1ee2fd2761518764ea7ae42d6e21816ca model-00015-of-00120.safetensors
|
| 144 |
+
b33392a8b5f66e82115d1c7bbe830945fdda9b5b3a2d5094c9e8f7120186d09f model-00016-of-00120.safetensors
|
| 145 |
+
b198f9d0606953932f131b3d1124488702280ca55627d5c9156823a87a56144b model-00017-of-00120.safetensors
|
| 146 |
+
b98e6fa60d19eeabe5f937dfa5cde62ee0cffb7e8687f7c6e8e54be797e76cfd model-00018-of-00120.safetensors
|
| 147 |
+
1ac4f98e4a16f9f620d83a51353a5b3ae4af05055b7777633981a930f063a9a4 model-00019-of-00120.safetensors
|
| 148 |
+
db5f5e4bfe9704b55cad6fddcbeb0b50c0f0d760af247f13ea78e6b9a13bf5e9 model-00020-of-00120.safetensors
|
| 149 |
+
94abdb9997af61ed92f43d58c73c96947add0ac1580c2255eaba2b254aa34782 model-00021-of-00120.safetensors
|
| 150 |
+
d1c09ce5b40bf2f976a6918188b10382735be8f9d936fc7d9e3d174d67fcb193 model-00022-of-00120.safetensors
|
| 151 |
+
e75296748bb0940bf8a45e3743444d46404a0984c25164212764211d07ea5c3a model-00023-of-00120.safetensors
|
| 152 |
+
f9fa4d4312b74a757befe4f488dab56370a9c21771a86c2b92e3ac74ed068863 model-00024-of-00120.safetensors
|
| 153 |
+
6f94c10adb857a39f5535be2b50b8037d08c798f13df4eb934fb9c14d44e9962 model-00025-of-00120.safetensors
|
| 154 |
+
0c65f0517b61444c2b9c243ed5a97a6b2aec76054da36c2ef23b7d0263d0f1f4 model-00026-of-00120.safetensors
|
| 155 |
+
af324d2631116bb4ca61b15578ac270874ce82035cb262e1adcb4056a76841b0 model-00027-of-00120.safetensors
|
| 156 |
+
5e031de67c3c86ca62170a72c996c1cbd588a3a36b5a026dd0823e088793f196 model-00028-of-00120.safetensors
|
| 157 |
+
bae837ec87eca4b857597783fc9035d4fa6e049addd4a1f46c052de1a31bb6da model-00029-of-00120.safetensors
|
| 158 |
+
bbe67349b26057fe942bfce9fdff6422a9b822924b2445bbde7c2ca4c974c707 model-00030-of-00120.safetensors
|
| 159 |
+
3aebbcb6ef80e74313e015c5ce828bb09fb02d65256e2567bf6bee2d1bdcf887 model-00031-of-00120.safetensors
|
| 160 |
+
cb9063b4790ac8ede704b801aabf416adfbcc2cb4c3cece85d499ffd710b5f00 model-00032-of-00120.safetensors
|
| 161 |
+
598e794cb00fcb85f20ff6e701e5a98a601b19681d77c11c370081b8836d0417 model-00033-of-00120.safetensors
|
| 162 |
+
fe68ce83f4b32da0cdc5f6803ae4ac977f2e0b007df627dce80ac93b3fd88817 model-00034-of-00120.safetensors
|
| 163 |
+
5c439c150741aa601c9f8f8a8f0bb650a59deb9d66e018e3e003baf97551a0e2 model-00035-of-00120.safetensors
|
| 164 |
+
bba2d7a1df63672dd6400c74bd07352698bc9e74497812963d76f6c379a4fcb7 model-00036-of-00120.safetensors
|
| 165 |
+
39ac664b9906d0970fedd207c20fe8b54cd16703ba765db4de332f2f3a4ae24f model-00037-of-00120.safetensors
|
| 166 |
+
26b70b8b61940befb31f4bf5956dfd9dc89500bddb36b4e79eb046ab39000412 model-00038-of-00120.safetensors
|
| 167 |
+
c2599dd1cdc04c23c3383ecccad7d50d11d0e52c6602574bc1da2428e84f7028 model-00039-of-00120.safetensors
|
| 168 |
+
e109dbf65aba7827809cc67334065364a1684715179b31311e0a31c00d8dd48c model-00040-of-00120.safetensors
|
| 169 |
+
bb85b908911687c0e276e43e81b5a4a265a87c00965324532681097ca491bc9f model-00041-of-00120.safetensors
|
| 170 |
+
a017885e30064729e5f70cbaeda434663c6c382aa9d2ba2a73f820a8cd5b6bbe model-00042-of-00120.safetensors
|
| 171 |
+
04f3aeccb23c779ac9e6be9e952418b95cac19327e7873dce5f69e4ad728f6e0 model-00043-of-00120.safetensors
|
| 172 |
+
fa5a385f7a8220ed99bc081f2fe26a07c480a3da7f497be57cf95f96ceb1c34c model-00044-of-00120.safetensors
|
| 173 |
+
b0b508dbd6217fe861a68bd8890415fc0f97404ed9ea7155adae9476b12c27d0 model-00045-of-00120.safetensors
|
| 174 |
+
c0e27a8d0a9e4521e96f17ad0e40de8152ddd8c26f7739520cc052069e4432f7 model-00046-of-00120.safetensors
|
| 175 |
+
5c471e0726598af910a1d5c895eca9bf41afe1e45311ee0dc962572b21097f2d model-00047-of-00120.safetensors
|
| 176 |
+
1d957114322b3d9dbb4cca73d9bba9cf628ddc7f5a6dd1d6af6224eb6aff003e model-00048-of-00120.safetensors
|
| 177 |
+
895d22c61a559667d84ac65dcd3012de18103738d3660c0c84db0b90e29c7e32 model-00049-of-00120.safetensors
|
| 178 |
+
c2bd099b71e35b8af36785f23e025c8b3ce393197b801cd3e53ee33e9be71531 model-00050-of-00120.safetensors
|
| 179 |
+
1c30583b2edd80ed55db4086e1b846f7a9de54f7996bfa07e2ac0039f702c10a model-00051-of-00120.safetensors
|
| 180 |
+
7c800f2f476cff08606cc3b036571d43ea501518fcba658aeda5db0fce815346 model-00052-of-00120.safetensors
|
| 181 |
+
43acf4bbb16dda6f5a7c31240108bad19c64ac5eb9d20022321c4eeea48bc991 model-00053-of-00120.safetensors
|
| 182 |
+
98bb70809af0d4d3177a377eefcbf728dd7fbd9e3891f130c05c66d29cd04860 model-00054-of-00120.safetensors
|
| 183 |
+
285c605b3391fbca1a40f28fdb6c1856a155ccfe6de43eb59e11033360dadf51 model-00055-of-00120.safetensors
|
| 184 |
+
6f825c25cd071930e7cbec832fe71a65767024b6853bdf554a9dd6534d0cd25c model-00056-of-00120.safetensors
|
| 185 |
+
9c7e6d5fd6d4138fde51eef51a83ff8a33424294c44d52d1b62b42ddb79ada4d model-00057-of-00120.safetensors
|
| 186 |
+
0b73d4dc1f373046f08a000d60484a7cf5335c4b0a1e4588d0cf8089f3ed75f2 model-00058-of-00120.safetensors
|
| 187 |
+
c5e83b54c3f1912aa5fd35f1c3170415f54708887f6f358cb1eac0b93391394d model-00059-of-00120.safetensors
|
| 188 |
+
c37b7e82d16a82243664ecac9e417a815195e1c26d9affe2ce681bffcb2fa046 model-00060-of-00120.safetensors
|
| 189 |
+
ec26e65bf26f2dadf4e4c7aeefe48acbd9b677a3d03ba0d3229f1cc796647bb4 model-00061-of-00120.safetensors
|
| 190 |
+
98c6427e9b22529ea985aa5d343e24055d3fd570b866e4d8dfb8f0b9470caedb model-00062-of-00120.safetensors
|
| 191 |
+
dacdd78b8a4f74ea5502d28b0a8d0c632a05252e095abc6245f0a63bdbd57a46 model-00063-of-00120.safetensors
|
| 192 |
+
7f134d623d5f229cb4732905ceb269404e1b00a72b8c25cc4f09d1e8610aabc5 model-00064-of-00120.safetensors
|
| 193 |
+
16661291a3d218f765edcb3831ff9426af21d6d56e6d8082b194e5cac9348109 model-00065-of-00120.safetensors
|
| 194 |
+
e8c5392e733bd98d5d9f38d47e18cf6834bcbc0b8d24937c42d36ebc60604910 model-00066-of-00120.safetensors
|
| 195 |
+
f2fb7876facab2bbb569fe94ab41dd1e71c50a3811853bf37688e5ebdc5813c0 model-00067-of-00120.safetensors
|
| 196 |
+
abc3009f10522e97eedbcd34903e3aafa55f7d72efa4ff3db4b5c4fb28259ca1 model-00068-of-00120.safetensors
|
| 197 |
+
ee4f3cb631ca5b0552fab89a3ef05ed0d8dab5ee0066fa245af479c962071e53 model-00069-of-00120.safetensors
|
| 198 |
+
8b60971fe302e1bcf1bb7da900276a3f26fcd38b10d6760330ee59ea8df7a589 model-00070-of-00120.safetensors
|
| 199 |
+
9fb9f3581b0a09d43e94e0476b9fa0971b3ab30b3d980bf6bb0814fb92db82d4 model-00071-of-00120.safetensors
|
| 200 |
+
caec81374ed646f0d7953f81565438ce18dfc11197cbb1a442b557cfb907dd23 model-00072-of-00120.safetensors
|
| 201 |
+
313e11a85717e5fe56c6901f16c10aab760619b7655d355185a17dcb6986397a model-00073-of-00120.safetensors
|
| 202 |
+
3c22a6012bb34c264869fd8a93ac019175f22d9d255cdb1987d25572f6ebcb37 model-00074-of-00120.safetensors
|
| 203 |
+
55992a5b643dad0a06c6bd8e4a13bac8980d31f3e773d56cc86a0c36543c02e5 model-00075-of-00120.safetensors
|
| 204 |
+
3dea15fd98ef8c45a23876a3dd1f3f6c06feb6c5e3a35b29dde93a82196e9f63 model-00076-of-00120.safetensors
|
| 205 |
+
fb6efae4033a54282fa0f779ce41dd64edff903621e829e23c28f2423f032059 model-00077-of-00120.safetensors
|
| 206 |
+
1f231a1c8149307a14c940df734637e03b0cad5db40e4165c69b9a5e817471d9 model-00078-of-00120.safetensors
|
| 207 |
+
84eb7df2440c3fe0cad633bfdb05fc1f9f4a69514d33a3c48e08f751e8729b03 model-00079-of-00120.safetensors
|
| 208 |
+
1008d2f310da3804fe85ca18c86f7f40c7e7005afe3edd0e17bff9207edb15cd model-00080-of-00120.safetensors
|
| 209 |
+
30bb97e87009412ec4445773ab5dde2267f395290a37ea204b0b94fb78b06c16 model-00081-of-00120.safetensors
|
| 210 |
+
6314d366c0a954e7a8beed9ead7389dc5c9ec6a78667f63778edb68a1ec5a5a7 model-00082-of-00120.safetensors
|
| 211 |
+
424127fa0a70e70d5073200c3ede1097dc31345b67309e9c1ca14fc1d2c601c9 model-00083-of-00120.safetensors
|
| 212 |
+
8f5be1a45eeafd520fa3e0adceffb15cceaa6510c61d032475f017966885c248 model-00084-of-00120.safetensors
|
| 213 |
+
22722c615db28a151620c8a6d11714f6de26bc386f3d495a89666191122ee1bf model-00085-of-00120.safetensors
|
| 214 |
+
dcd025dc9e48dd3744996a3b9d337d3c8636c46591e80ab6cd489aac78b61b76 model-00086-of-00120.safetensors
|
| 215 |
+
c00bdc2031dd9c042fcf56613979d029256c2c337c6dddf61f6e5efe18e6c883 model-00087-of-00120.safetensors
|
| 216 |
+
147abc2a72e6eea5941cd03175febb962a81d068a2872c6594bef6f3e5535007 model-00088-of-00120.safetensors
|
| 217 |
+
5d2ec3fd271c7a2ec4161f4e5bf2c14eef7f549ae1a6d39bb666614e26d2dfc1 model-00089-of-00120.safetensors
|
| 218 |
+
2bb2e9ab785905faaeeb8b6a3df7ff2f7ae246fb1e3f51fd4cfd0b3af94e44f1 model-00090-of-00120.safetensors
|
| 219 |
+
8ccbc08c9dbcd37d1ec6cfd772667b12cd521bfd4dc05113e7e9f0ce1d2b9603 model-00091-of-00120.safetensors
|
| 220 |
+
20422d9ea7e0997852897daf8e6c84b420c9c8b3a700b301f416091cdb8e4efb model-00092-of-00120.safetensors
|
| 221 |
+
753329dbb8d2b4efc7cf4ca7fe9b1455e0128cbe725a98fc91a05fcf38d616d9 model-00093-of-00120.safetensors
|
| 222 |
+
f08e09cb29e5c1ffc05b128d581e815914ced8f30949a1e2e114470dbbba2dbd model-00094-of-00120.safetensors
|
| 223 |
+
c08536722ef0a2cecb5a5d843fd0086275c329918eff733676af1cb6bdb99abb model-00095-of-00120.safetensors
|
| 224 |
+
fafe3a925e795c9b570c8eefeb0ea492df97e941f23e2e7e8206ded8cd28933d model-00096-of-00120.safetensors
|
| 225 |
+
e616de880c9587930ecf502799255189c0f03e102273ade47dbc104c136e92c5 model-00097-of-00120.safetensors
|
| 226 |
+
7c34ac45ffba12a049c0b563cd002a50d1fb6ea9dc80c6c23203c43d5768705c model-00098-of-00120.safetensors
|
| 227 |
+
9693db794a7992532a96bd0ea185a49a76c123fb50f7bcf4fff0902847e0c6ed model-00099-of-00120.safetensors
|
| 228 |
+
a19fb303a9814d5082fc3fd5d69127bc6724bf574276e6d18918b56ab46e8eee model-00100-of-00120.safetensors
|
| 229 |
+
c24ca6ae7ce061eba28d302b878700d30ec22656a69a3db53bbb4ac4ad1ae961 model-00101-of-00120.safetensors
|
| 230 |
+
b5c8396800acefb6d0bc7a0a48cd055d28faa94dc2bb34f1867a58ec83991028 model-00102-of-00120.safetensors
|
| 231 |
+
09139292ba5af27e66767a6860a53b7693f091c513bac154c32d44b408783738 model-00103-of-00120.safetensors
|
| 232 |
+
b0af3c5b38f078c72d405319fb47ca4c7ef74324266106ca3962ae63dc6e0a28 model-00104-of-00120.safetensors
|
| 233 |
+
f475efcce530b46749e9ece468b65a5aa65a073b7fcec00c22aec5b02e4e7782 model-00105-of-00120.safetensors
|
| 234 |
+
1059fe4a53c233b1fdb28ae431d61190c6491f55223c58c0660dfead53be7bc9 model-00106-of-00120.safetensors
|
| 235 |
+
57e1f99044388f5c0e5b422d678e9477475dc170dcc97b654f931e3ef9facfee model-00107-of-00120.safetensors
|
| 236 |
+
286de5d27896da595638cc5aae2e390bd7676e0d450f490842c165975febff5f model-00108-of-00120.safetensors
|
| 237 |
+
b41955ae5e684cff92ae005d27b626b2abbe72fc1ad0e9b273b6d71ca3a74b66 model-00109-of-00120.safetensors
|
| 238 |
+
b2bd23f9b6d11910f9b7f9e4951ae254bc64c1844a2541de48cd4ac6755d0abc model-00110-of-00120.safetensors
|
| 239 |
+
091e1d588b899c5369b713a053a7bc76ad35bc93a81af20961167f89bc953b1f model-00111-of-00120.safetensors
|
| 240 |
+
8a7c2486aa93f1c952da2ffd6137f2f38b443e21923c5f640edcaab6c340d4a1 model-00112-of-00120.safetensors
|
| 241 |
+
8f3205a5dd7e9162c2ec89e19b82c10fcdd41f716b101322833f33cc27aed753 model-00113-of-00120.safetensors
|
| 242 |
+
e3cf6f9ea4e07df98a3bc4cbac85f9228ff3e9fd45f02f8735672d27f81a9404 model-00114-of-00120.safetensors
|
| 243 |
+
347b2e08fca7a5bbe63e39e2bd225fb0af5a7ff318120cf7e5289c4ee2a72c23 model-00115-of-00120.safetensors
|
| 244 |
+
3d9c0120686a8db80f7c6bd5c1044e737a4a8f5dc8f4111038308aa0cf80d88f model-00116-of-00120.safetensors
|
| 245 |
+
48eca9375a20c8f8c6435ea6e4ee709238680873967408dc0ba499179e7b7da5 model-00117-of-00120.safetensors
|
| 246 |
+
f7df8825ffec03ac813ecfabc62f9a416c8884d11aa35da655f322a6b6870817 model-00118-of-00120.safetensors
|
| 247 |
+
8e393cfce320eba98c19f69574725ab1aa5153d710eaa651df5e2c35bfeca1ae model-00119-of-00120.safetensors
|
| 248 |
+
15ffa59ac9a36b7f0fbfabdea893dfc6b6db4dd921d7a291b0056062d6937dda model-00120-of-00120.safetensors
|
| 249 |
+
2f64d21c67c90bbafeb36c4e9b2f06f54063ed439e9f7cf95962d425a1d8515d model.safetensors.index.json
|
| 250 |
+
aae38374c94b08cc9b0547c6e64f05b951bd9735cea571c6988f5ed552bed3ed processor_config.json
|
| 251 |
+
5d9aadd98432da86c628866dfd2944084a5b00f208bcbf7de10518994159e699 provenance/source-model-revision.json
|
| 252 |
+
22bcfacf0f7e492ecd571f6f19819656ede91b6cfc1c0abb4ae5bf4a0a0df8e5 quantization/recipe.json
|
| 253 |
+
1e5cdf56da1929c2a7d8982085324de05bb92144882792f034239e289316c645 quantization_config.json
|
| 254 |
+
efeaed9daed88eb567df5518f1418f0ebbe5c03132a35e70f7506ab90a04efbd receipts/checkpoint.json
|
| 255 |
+
a0e1969f977f926a4477db8fbad174a9ecdfab62c547b051e0e6cd5e3c79bd2a runtime/scripts/qualify_glm53_custom_tp2_runtime.py
|
| 256 |
+
7c83b2785d338dcaff8a5fe3b87b2957dea6ea8c1c31ddb02465b797f4c4647e runtime/scripts/run_glm53_custom_tp_runtime.py
|
| 257 |
+
c1d7fb3aaa98d2baa24064ad19c91336ecbb89b35e446500e06bff9a1b61045e runtime/src/quant_pipeline/__init__.py
|
| 258 |
+
01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/allocation/__init__.py
|
| 259 |
+
09fa93212c4400f5dba4f47be95a22a52d97c923a0889e2c104e4f54581e0a2e runtime/src/quant_pipeline/allocation/global_dp.py
|
| 260 |
+
b9e11264f116c3ee55d30f4c35080d7e266851b4325742b8e4a58f7e328d5ea7 runtime/src/quant_pipeline/calibration/__init__.py
|
| 261 |
+
038a40c84ee23ab46e8d7e7aefa92fcfe4008c9d0c7204c006481f9c1ce50ba8 runtime/src/quant_pipeline/calibration/fitter.py
|
| 262 |
+
ccb49f16c91b37dcbd235af18de422a7b54773d32f11a3a958172b984fb6c161 runtime/src/quant_pipeline/calibration/glm53_capture.py
|
| 263 |
+
c91df3cc86363a325a2334291ba6171cf2d5e9cab2d29bb7b09268c0795c2d05 runtime/src/quant_pipeline/calibration/glm53_mtp_capture.py
|
| 264 |
+
86a6f7dc01961f6117a880c4dc8ce394af6a31ce821aa32d73ffcf091e2d3a73 runtime/src/quant_pipeline/calibration/qwen_capture.py
|
| 265 |
+
84f3bc4042b36f9002ab49084ad5629c627ab4e2415651d416b9f96c4e1ca0b4 runtime/src/quant_pipeline/calibration/route_mass.py
|
| 266 |
+
87cab3cc4dce85458d61e93baac28dde9735335ba2c9b84178675b40d313a08c runtime/src/quant_pipeline/calibration/windows.py
|
| 267 |
+
6757125714ce3d4e6eb60cb73329d9bb6f64cb20ddf86306b4176a7159c0339f runtime/src/quant_pipeline/campaign/__init__.py
|
| 268 |
+
de572b6e6b88f00c183e26889fceaf1e94baed151cf3a372a3a5cfc8fbc663de runtime/src/quant_pipeline/campaign/glm53_direct_k4.py
|
| 269 |
+
fc5b456893454b2fa0dc234e3837a7894f48cda3cd96bcabfa22071e54f7552d runtime/src/quant_pipeline/campaign/glm53_mcg_preparation.py
|
| 270 |
+
03bd9f5720a9c19e0dc5026fa78068fbc5fc433056a8a43b3e5e5427ff01627c runtime/src/quant_pipeline/campaign/glm53_mtp_k4.py
|
| 271 |
+
5b196e0892638f76d1a0a6d8cfacff8f7dcff28d10e1ae0c5636532275b34f9e runtime/src/quant_pipeline/campaign/glm53_prepared_backend.py
|
| 272 |
+
f93faa0909667e3a71aab46fcc0fde52a7c724d7803cf68e32404ee0f65e29ec runtime/src/quant_pipeline/campaign/glm53_provenance.py
|
| 273 |
+
a60fbee62ef2faf45b56157c8624a522deb72f6a325450d58c8ab395c4ba7ba5 runtime/src/quant_pipeline/campaign/glm53_uniform_k4.py
|
| 274 |
+
85d0c3644e380c2ba9017ca199a8caa77b719f4a7df6103e08ba8d3a9db13500 runtime/src/quant_pipeline/campaign/glm_contract.py
|
| 275 |
+
1e2d5fed6d8ef3b6f354aa572646df37ba03065d9102cb60dfd07b2a84a5fedc runtime/src/quant_pipeline/campaign/qwen_adapter.py
|
| 276 |
+
70c0a088047164a98d830271e724a5e2ad36647ffaaccdd80111dc47aaf29854 runtime/src/quant_pipeline/campaign/qwen_attribution.py
|
| 277 |
+
e8fd5f74f704b2b486bfe85093198c3b049125faafbe4e8f97ca14cea7babc5c runtime/src/quant_pipeline/campaign/qwen_services.py
|
| 278 |
+
a70323b46d86988bd27b5186d4d0103d1b0d0b755e636def7ffd4c7e2cbf2d5e runtime/src/quant_pipeline/campaign/qwen_work_units.py
|
| 279 |
+
20e96288b145594e43428a91965f85979ffc7163688d286cd6867ee20dc07ba8 runtime/src/quant_pipeline/campaign/runner.py
|
| 280 |
+
4c74cc45cdefef59f07265d5996ea4123ba26fe6e69dbc5f54091c0082f47a8b runtime/src/quant_pipeline/candidates/__init__.py
|
| 281 |
+
96fca4dbe7d85be45052d18c18ed461ad2c2303467c2cbe4b60b425e99813f36 runtime/src/quant_pipeline/candidates/factory_allocation.py
|
| 282 |
+
c5b012837a683106ea21e4e52d5f7853e14eb414f9cc9f37047ebf94e6f9a0c1 runtime/src/quant_pipeline/candidates/factory_calibration.py
|
| 283 |
+
2d6a1825c23b8149e208db26f302e4c2bf1339e3ec6f51e0784913f15c62b212 runtime/src/quant_pipeline/candidates/factory_union.py
|
| 284 |
+
f313d706df03efb5bd7fd06dd1a88ef749170ada6546240bfb02bced07d47162 runtime/src/quant_pipeline/candidates/ledger.py
|
| 285 |
+
e6450a45cffacc49da22bfdc1eeac70d4a00b884e5525af88a9293b4d46874d6 runtime/src/quant_pipeline/candidates/payload_store.py
|
| 286 |
+
30366e8c26f27b770fabc6325b833bea97f32087a7e9b48bf0605f5f36ba5a9a runtime/src/quant_pipeline/capacity/__init__.py
|
| 287 |
+
f45ef249c48131005580714888397840400f81e3c6f0caf023382fef1a8ab882 runtime/src/quant_pipeline/capacity/mla.py
|
| 288 |
+
97f1d9bb510eb7be6aeaac52e627ab645de01f3ff2788045087ce0e876ed9cdc runtime/src/quant_pipeline/checkpoint/__init__.py
|
| 289 |
+
74cf73cf90a2c8d42e071ab10da343b3e4ae3666c848703287b53706d91adbcb runtime/src/quant_pipeline/checkpoint/btx_qwen.py
|
| 290 |
+
65fd985494625d2b1e0541ae9106d57a085063f22df1cce2bf0307b63970e47b runtime/src/quant_pipeline/checkpoint/exact_payload.py
|
| 291 |
+
a9aad8da1d228afaf2edd2cd84a6657ac5b53628554e89918484aca20d602d22 runtime/src/quant_pipeline/checkpoint/glm53_mcg_materializer.py
|
| 292 |
+
19466f3fd531a03f7c43fabafe2ca395981d47cdd1ed818630a5fb80213817b6 runtime/src/quant_pipeline/checkpoint/official_btx.py
|
| 293 |
+
36f84586d89ae7816172929471a35dc81ec5beff63c47a68cca862b61a1dc4dc runtime/src/quant_pipeline/checkpoint/packed_payload.py
|
| 294 |
+
4a00fffddd2993998af7c3c660efbfdc4ea0abff96a17d60db969f193b3a60cb runtime/src/quant_pipeline/checkpoint/reference_pack.py
|
| 295 |
+
d75754a774f21054d221b76ca82863da1fc343d409107eb925cf64ec67456fb9 runtime/src/quant_pipeline/cli.py
|
| 296 |
+
01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/codecs/__init__.py
|
| 297 |
+
13263a584125d0600a580eb63ba7397d4abcb70d8a0be4a3fb328c9ec53741cd runtime/src/quant_pipeline/codecs/exl3_mcg.py
|
| 298 |
+
5615adfb8e39407a18b6ad6e4942985e14483eae7b4604bf6b85111441e1e652 runtime/src/quant_pipeline/codecs/protocols.py
|
| 299 |
+
a1910785f0bab4f498563f135984f55c68735869203fd8d076ffcd9ced67c5ed runtime/src/quant_pipeline/codecs/uniform.py
|
| 300 |
+
01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/core/__init__.py
|
| 301 |
+
b50023ac37b2c1bf78a1497e243ad8dab11330a45b12c2fd1d99af17470e2087 runtime/src/quant_pipeline/core/artifacts.py
|
| 302 |
+
a85ccafbfb64a7bb4dafd254966fcc5864056b6ed60dd68acee6b6a91a8092ed runtime/src/quant_pipeline/evaluation/__init__.py
|
| 303 |
+
22d0bb24bd350cfe45f701a5f6e57bd0e6f17666ae8b7538abfc54b1d4845a3d runtime/src/quant_pipeline/evaluation/glm53_logits.py
|
| 304 |
+
994b211f75411a5b8a0f9242559adfa062c28284e5cf8b215df8dedef8e0583d runtime/src/quant_pipeline/evaluation/glm53_packed_k4_reader.py
|
| 305 |
+
dbe98e31e77e6feb60c95b8f1eaa2825328b28515be6add492fc88e5fa6b9567 runtime/src/quant_pipeline/evaluation/kld_window.py
|
| 306 |
+
01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/models/__init__.py
|
| 307 |
+
72f151f612f4ed5d4de7f1dd4b2a0f63c3a787ad7d6a0a966cfbc3524cc20525 runtime/src/quant_pipeline/models/hf_capture.py
|
| 308 |
+
c2f8344e2fe62de4a4b95b9e27866877005e268894ea52ca6218cb0ca16a7300 runtime/src/quant_pipeline/models/inventory.py
|
| 309 |
+
8611f5f6678017e1e32f934ae659106eeed5ac376236450156616a0d56366a96 runtime/src/quant_pipeline/normalization/__init__.py
|
| 310 |
+
eb3dd0ea0a59e16085ffe116d692af6de84f0d6bb8a5482a69290a175c44ea57 runtime/src/quant_pipeline/normalization/absolute_v31.py
|
| 311 |
+
d257d5f5e04ed7c2ec6341050c12015d0038428381d807505966139ba00276bf runtime/src/quant_pipeline/normalization/artifact_v31.py
|
| 312 |
+
d883764ff24fd2de0e7809bde20e3f33d0c8aa1122929b37e4b843912a85a1d5 runtime/src/quant_pipeline/normalization/prior_search.py
|
| 313 |
+
573ded7f185316ecaf5a581f05b102b56d546e20bc1fd5a4dc73feae9a951da2 runtime/src/quant_pipeline/normalization/streaming_v31.py
|
| 314 |
+
fdf134e41cc7ab16d894259271eab7149131fa7ffdac7676f73c18b6edf6dac3 runtime/src/quant_pipeline/publication/__init__.py
|
| 315 |
+
bc561815be7a34e3f482aa372cc7e6600c3b368813e3fcfac42c400f8b36ac23 runtime/src/quant_pipeline/publication/glm53_hf.py
|
| 316 |
+
49f9917cb9b88fbeab1d6b88ab75b9d830a4a563d8f9f89490a2e9efc5bfecc2 runtime/src/quant_pipeline/publication/glm53_k4_postmtp.py
|
| 317 |
+
0bc92706bc208a322b806de849e57b8ac7c39d3c90ed86934de6e69414b0ac07 runtime/src/quant_pipeline/results/__init__.py
|
| 318 |
+
44fb83ded80dca0e115d9285b2143053339bbc4231f9fd112c739898637bc696 runtime/src/quant_pipeline/results/ledger.py
|
| 319 |
+
c44558f4859c2111808ab02d654214ce02a1a8d7559030cb7d684700d76a50c4 runtime/src/quant_pipeline/runtime/__init__.py
|
| 320 |
+
11617266954541f4c7337bf90501ec6f540ae3a346c486d90bdbe3380d625e67 runtime/src/quant_pipeline/runtime/glm53_tp2_exl3.py
|
| 321 |
+
01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/scoring/__init__.py
|
| 322 |
+
ff386c0463d135b1b6e3c67f5daa0c16847b206253727edd6a453386ae1d4be3 runtime/src/quant_pipeline/scoring/attribution.py
|
| 323 |
+
8beb3e84f9f11263bcc2b69bc122cdf09133289cdc992a6d75cef7c4137c5859 runtime/src/quant_pipeline/scoring/blend.py
|
| 324 |
+
a43789dc10a504c8f3b13a9e760e323e443e95c551579a54b6fa1c210ffe0d3b runtime/src/quant_pipeline/scoring/kld.py
|
| 325 |
+
fc2787667aef44bf1705b1ef4bb7bae984782c92ac69ff314ab8bf60f471f80f runtime/src/quant_pipeline/scoring/qwen_experts.py
|
| 326 |
+
9aed1d7b7aa54f1d65ad01a61d1b50de20fb3c6ec90644a25e49e317628707fd runtime/src/quant_pipeline/spec.py
|
| 327 |
+
19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d tokenizer.json
|
| 328 |
+
98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc tokenizer_config.json
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,210 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[gMASK]<sop>
|
| 2 |
+
{%- set effective_reasoning_effort = reasoning_effort if reasoning_effort is defined and reasoning_effort in ['low', 'high'] else 'max' -%}
|
| 3 |
+
{%- if effective_reasoning_effort is not none -%}<|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}
|
| 4 |
+
{%- if tools -%}
|
| 5 |
+
{%- macro tool_to_json(tool) -%}
|
| 6 |
+
{%- set ns_tool = namespace(first=true) -%}
|
| 7 |
+
{{ '{' -}}
|
| 8 |
+
{%- for k, v in tool.items() -%}
|
| 9 |
+
{%- if k != 'defer_loading' and k != 'strict' -%}
|
| 10 |
+
{%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}
|
| 11 |
+
{%- set ns_tool.first = false -%}
|
| 12 |
+
"{{ k }}": {{ v | tojson(ensure_ascii=False) }}
|
| 13 |
+
{%- endif -%}
|
| 14 |
+
{%- endfor -%}
|
| 15 |
+
{{- '}' -}}
|
| 16 |
+
{%- endmacro -%}
|
| 17 |
+
{%- macro tool_references_to_response(refs) -%}
|
| 18 |
+
{{- '<tool_response><tools>\n' -}}
|
| 19 |
+
{%- for tr in refs -%}
|
| 20 |
+
{%- for tool in tools -%}
|
| 21 |
+
{%- if 'function' in tool -%}
|
| 22 |
+
{%- set tool = tool['function'] -%}
|
| 23 |
+
{%- endif -%}
|
| 24 |
+
{%- if tool.name == tr.name -%}
|
| 25 |
+
{{- tool_to_json(tool) + '\n' -}}
|
| 26 |
+
{%- endif -%}
|
| 27 |
+
{%- endfor -%}
|
| 28 |
+
{%- endfor -%}
|
| 29 |
+
{{- '</tools></tool_response>' -}}
|
| 30 |
+
{%- endmacro -%}
|
| 31 |
+
<|system|>
|
| 32 |
+
# Tools
|
| 33 |
+
|
| 34 |
+
You may call one or more functions to assist with the user query.
|
| 35 |
+
|
| 36 |
+
You are provided with function signatures within <tools></tools> XML tags:
|
| 37 |
+
<tools>
|
| 38 |
+
{% for tool in tools %}
|
| 39 |
+
{%- if 'function' in tool -%}
|
| 40 |
+
{%- set tool = tool['function'] -%}
|
| 41 |
+
{%- endif -%}
|
| 42 |
+
{% if tool.defer_loading is not defined or not tool.defer_loading %}
|
| 43 |
+
{{ tool_to_json(tool) }}
|
| 44 |
+
{% endif %}
|
| 45 |
+
{% endfor %}
|
| 46 |
+
</tools>
|
| 47 |
+
|
| 48 |
+
For each function call, output the function name and arguments within the following XML format:
|
| 49 |
+
<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
|
| 50 |
+
{%- macro visible_text(content) -%}
|
| 51 |
+
{%- if content is string -%}
|
| 52 |
+
{{- content }}
|
| 53 |
+
{%- elif content is iterable and content is not mapping -%}
|
| 54 |
+
{%- for item in content -%}
|
| 55 |
+
{%- if item is mapping and item.type == 'text' -%}
|
| 56 |
+
{{- item.text }}
|
| 57 |
+
{%- elif item is string -%}
|
| 58 |
+
{{- item }}
|
| 59 |
+
{%- elif item is mapping and item.type in ['image', 'image_url', 'video', 'video_url', 'audio', 'audio_url', 'input_audio'] -%}
|
| 60 |
+
{%- set media_type = item.type | replace('_url', '') | replace('input_', '') -%}
|
| 61 |
+
{{- "<reminder>You are unable to process this " ~ media_type ~ " because you don't have multi-modal input ability. Try different methods.</reminder>" }}
|
| 62 |
+
{%- endif -%}
|
| 63 |
+
{%- endfor -%}
|
| 64 |
+
{%- else -%}
|
| 65 |
+
{{- content }}
|
| 66 |
+
{%- endif -%}
|
| 67 |
+
{%- endmacro -%}
|
| 68 |
+
{%- macro tool_response(text) -%}
|
| 69 |
+
{{- '<tool_response>' + text + '</tool_response>' -}}
|
| 70 |
+
{%- endmacro -%}
|
| 71 |
+
{%- macro render_tool_response(m) -%}
|
| 72 |
+
{%- if m.content is string -%}
|
| 73 |
+
{{- tool_response(m.content) -}}
|
| 74 |
+
{%- elif m.content and m.content is not mapping and m.content.0.type == "tool_reference" -%}
|
| 75 |
+
{{- tool_references_to_response(m.content) -}}
|
| 76 |
+
{%- elif is_list_of_outputs(m) -%}
|
| 77 |
+
{%- for tr in m.content -%}
|
| 78 |
+
{%- if tr.output is iterable and tr.output is not string and tr.output is not mapping and tr.output and tr.output.0.type == "tool_reference" -%}
|
| 79 |
+
{{- tool_references_to_response(tr.output) -}}
|
| 80 |
+
{%- else -%}
|
| 81 |
+
{{- tool_response(visible_text(tr.output)) -}}
|
| 82 |
+
{%- endif -%}
|
| 83 |
+
{%- endfor -%}
|
| 84 |
+
{%- else -%}
|
| 85 |
+
{{- tool_response(visible_text(m.content)) -}}
|
| 86 |
+
{%- endif -%}
|
| 87 |
+
{%- endmacro -%}
|
| 88 |
+
{%- macro id_of(obj) -%}
|
| 89 |
+
{%- if obj.tool_call_id -%}
|
| 90 |
+
{{- obj.tool_call_id -}}
|
| 91 |
+
{%- elif obj.id -%}
|
| 92 |
+
{{- obj.id -}}
|
| 93 |
+
{%- endif -%}
|
| 94 |
+
{%- endmacro -%}
|
| 95 |
+
{%- macro is_list_of_outputs(m) -%}
|
| 96 |
+
{%- if m.content and m.content.0.output is defined -%}1{%- endif -%}
|
| 97 |
+
{%- endmacro -%}
|
| 98 |
+
{%- set ns = namespace(last_user_index=-1) -%}
|
| 99 |
+
{%- for m in messages %}
|
| 100 |
+
{%- if m.role == 'user' %}
|
| 101 |
+
{%- set ns.last_user_index = loop.index0 -%}
|
| 102 |
+
{%- endif %}
|
| 103 |
+
{%- endfor %}
|
| 104 |
+
{%- for m in messages -%}
|
| 105 |
+
{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
|
| 106 |
+
{%- elif m.role == 'assistant' -%}
|
| 107 |
+
<|assistant|>
|
| 108 |
+
{%- set content = visible_text(m.content) %}
|
| 109 |
+
{%- if m.reasoning_content is string %}
|
| 110 |
+
{%- set reasoning_content = m.reasoning_content %}
|
| 111 |
+
{%- elif '</think>' in content %}
|
| 112 |
+
{%- set reasoning_content = content.split('</think>')[0].split('<think>')[-1] %}
|
| 113 |
+
{%- set content = content.split('</think>')[-1] %}
|
| 114 |
+
{%- endif %}
|
| 115 |
+
{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}
|
| 116 |
+
{{ '<think>' + reasoning_content + '</think>'}}
|
| 117 |
+
{%- else -%}
|
| 118 |
+
{{ '<think></think>' }}
|
| 119 |
+
{%- endif -%}
|
| 120 |
+
{%- if content.strip() -%}
|
| 121 |
+
{{ content.strip() }}
|
| 122 |
+
{%- endif -%}
|
| 123 |
+
{% if m.tool_calls %}
|
| 124 |
+
{% for tc in m.tool_calls %}
|
| 125 |
+
{%- if tc.function %}
|
| 126 |
+
{%- set tc = tc.function %}
|
| 127 |
+
{%- endif %}
|
| 128 |
+
{{- '<tool_call>' + tc.name -}}
|
| 129 |
+
{% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}
|
| 130 |
+
{% endif %}
|
| 131 |
+
{%- elif m.role == 'tool' -%}
|
| 132 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 133 |
+
{{- '<|observation|>' -}}
|
| 134 |
+
{%- set block_start = loop.index0 -%}
|
| 135 |
+
{%- set ns_blk = namespace(end=block_start) -%}
|
| 136 |
+
{%- for j in range(block_start, messages|length) -%}
|
| 137 |
+
{%- if messages[j].role == 'tool' -%}
|
| 138 |
+
{%- set ns_blk.end = j -%}
|
| 139 |
+
{%- else -%}
|
| 140 |
+
{%- break -%}
|
| 141 |
+
{%- endif -%}
|
| 142 |
+
{%- endfor -%}
|
| 143 |
+
{%- set ns_a = namespace(tool_calls=none) -%}
|
| 144 |
+
{%- if block_start > 0 and messages[block_start - 1].role == 'assistant' and messages[block_start - 1].tool_calls -%}
|
| 145 |
+
{%- set ns_a.tool_calls = messages[block_start - 1].tool_calls -%}
|
| 146 |
+
{%- endif -%}
|
| 147 |
+
{%- set ns_chk = namespace(can_sort=true) -%}
|
| 148 |
+
{%- if not ns_a.tool_calls -%}
|
| 149 |
+
{%- set ns_chk.can_sort = false -%}
|
| 150 |
+
{%- else -%}
|
| 151 |
+
{%- for k in range(block_start, ns_blk.end + 1) -%}
|
| 152 |
+
{%- set m = messages[k] -%}
|
| 153 |
+
{%- if is_list_of_outputs(m) -%}
|
| 154 |
+
{%- for entry in m.content -%}
|
| 155 |
+
{%- set eid = id_of(entry) -%}
|
| 156 |
+
{%- if not eid -%}
|
| 157 |
+
{%- set ns_chk.can_sort = false -%}
|
| 158 |
+
{%- endif -%}
|
| 159 |
+
{%- endfor -%}
|
| 160 |
+
{%- else -%}
|
| 161 |
+
{%- set tk_id = id_of(m) -%}
|
| 162 |
+
{%- if not tk_id -%}
|
| 163 |
+
{%- set ns_chk.can_sort = false -%}
|
| 164 |
+
{%- endif -%}
|
| 165 |
+
{%- endif -%}
|
| 166 |
+
{%- endfor -%}
|
| 167 |
+
{%- for tc in ns_a.tool_calls -%}
|
| 168 |
+
{%- set tc_id = id_of(tc) -%}
|
| 169 |
+
{%- if not tc_id -%}
|
| 170 |
+
{%- set ns_chk.can_sort = false -%}
|
| 171 |
+
{%- endif -%}
|
| 172 |
+
{%- endfor -%}
|
| 173 |
+
{%- endif -%}
|
| 174 |
+
{%- if ns_chk.can_sort -%}
|
| 175 |
+
{%- for tc in ns_a.tool_calls -%}
|
| 176 |
+
{%- set tc_id = id_of(tc) -%}
|
| 177 |
+
{%- for k in range(block_start, ns_blk.end + 1) -%}
|
| 178 |
+
{%- set m = messages[k] -%}
|
| 179 |
+
{%- if is_list_of_outputs(m) -%}
|
| 180 |
+
{%- for entry in m.content -%}
|
| 181 |
+
{%- set eid = id_of(entry) -%}
|
| 182 |
+
{%- if eid == tc_id -%}
|
| 183 |
+
{%- if entry.output is iterable and entry.output is not string and entry.output is not mapping and entry.output and entry.output.0.type == "tool_reference" -%}
|
| 184 |
+
{{- tool_references_to_response(entry.output) -}}
|
| 185 |
+
{%- else -%}
|
| 186 |
+
{{- tool_response(visible_text(entry.output)) -}}
|
| 187 |
+
{%- endif -%}
|
| 188 |
+
{%- endif -%}
|
| 189 |
+
{%- endfor -%}
|
| 190 |
+
{%- else -%}
|
| 191 |
+
{%- set tk_id = id_of(m) -%}
|
| 192 |
+
{%- if tk_id == tc_id -%}
|
| 193 |
+
{{- render_tool_response(m) -}}
|
| 194 |
+
{%- endif -%}
|
| 195 |
+
{%- endif -%}
|
| 196 |
+
{%- endfor -%}
|
| 197 |
+
{%- endfor -%}
|
| 198 |
+
{%- else -%}
|
| 199 |
+
{%- for k in range(block_start, ns_blk.end + 1) -%}
|
| 200 |
+
{{- render_tool_response(messages[k]) -}}
|
| 201 |
+
{%- endfor -%}
|
| 202 |
+
{%- endif -%}
|
| 203 |
+
{% endif -%}
|
| 204 |
+
{%- elif m.role == 'system' -%}
|
| 205 |
+
<|system|>{{ visible_text(m.content) }}
|
| 206 |
+
{%- endif -%}
|
| 207 |
+
{%- endfor -%}
|
| 208 |
+
{%- if add_generation_prompt -%}
|
| 209 |
+
<|assistant|>{{- '<think>' -}}
|
| 210 |
+
{%- endif -%}
|
config.json
ADDED
|
@@ -0,0 +1,300 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Glm5NextForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"image_end_token_id": 154831,
|
| 6 |
+
"image_start_token_id": 154830,
|
| 7 |
+
"image_token_id": 154854,
|
| 8 |
+
"model_type": "glm5_next",
|
| 9 |
+
"quantization_config": {
|
| 10 |
+
"bits": 4,
|
| 11 |
+
"codebook": "mcg",
|
| 12 |
+
"head_bits": 16,
|
| 13 |
+
"non_routed_dtype_policy": "official_source_native",
|
| 14 |
+
"quant_method": "exl3",
|
| 15 |
+
"scope": "glm53_routed_experts_only",
|
| 16 |
+
"serving_reader_qualified": false,
|
| 17 |
+
"version": "0.0.43"
|
| 18 |
+
},
|
| 19 |
+
"text_config": {
|
| 20 |
+
"attention_bias": false,
|
| 21 |
+
"attention_dropout": 0.0,
|
| 22 |
+
"dtype": "bfloat16",
|
| 23 |
+
"eos_token_id": [
|
| 24 |
+
154820,
|
| 25 |
+
154827,
|
| 26 |
+
154829
|
| 27 |
+
],
|
| 28 |
+
"first_k_dense_replace": 3,
|
| 29 |
+
"hc_eps": 1e-06,
|
| 30 |
+
"hc_mult": 4,
|
| 31 |
+
"hc_sinkhorn_iters": 20,
|
| 32 |
+
"head_dim": 0,
|
| 33 |
+
"hidden_act": "silu",
|
| 34 |
+
"hidden_size": 4096,
|
| 35 |
+
"index_head_dim": 128,
|
| 36 |
+
"index_kpool": 4,
|
| 37 |
+
"index_kpool_always_select_tail": true,
|
| 38 |
+
"index_kpool_compress": true,
|
| 39 |
+
"index_n_heads": 32,
|
| 40 |
+
"index_share_for_mtp_iteration": true,
|
| 41 |
+
"index_topk": 2048,
|
| 42 |
+
"indexer_rope_interleave": true,
|
| 43 |
+
"indexer_types": [
|
| 44 |
+
"full",
|
| 45 |
+
"full",
|
| 46 |
+
"full",
|
| 47 |
+
"full",
|
| 48 |
+
"full",
|
| 49 |
+
"full",
|
| 50 |
+
"full",
|
| 51 |
+
"full",
|
| 52 |
+
"full",
|
| 53 |
+
"full",
|
| 54 |
+
"full",
|
| 55 |
+
"full",
|
| 56 |
+
"full",
|
| 57 |
+
"full",
|
| 58 |
+
"full",
|
| 59 |
+
"full",
|
| 60 |
+
"full",
|
| 61 |
+
"full",
|
| 62 |
+
"full",
|
| 63 |
+
"full",
|
| 64 |
+
"full",
|
| 65 |
+
"full",
|
| 66 |
+
"full",
|
| 67 |
+
"full",
|
| 68 |
+
"full",
|
| 69 |
+
"full",
|
| 70 |
+
"full",
|
| 71 |
+
"full",
|
| 72 |
+
"full",
|
| 73 |
+
"full",
|
| 74 |
+
"full",
|
| 75 |
+
"full",
|
| 76 |
+
"full",
|
| 77 |
+
"full",
|
| 78 |
+
"full",
|
| 79 |
+
"full",
|
| 80 |
+
"full",
|
| 81 |
+
"full",
|
| 82 |
+
"full",
|
| 83 |
+
"full",
|
| 84 |
+
"full",
|
| 85 |
+
"full",
|
| 86 |
+
"full",
|
| 87 |
+
"full",
|
| 88 |
+
"full"
|
| 89 |
+
],
|
| 90 |
+
"initializer_range": 0.02,
|
| 91 |
+
"intermediate_size": 12288,
|
| 92 |
+
"kv_lora_rank": 512,
|
| 93 |
+
"layer_types": [
|
| 94 |
+
"linear_attention",
|
| 95 |
+
"linear_attention",
|
| 96 |
+
"linear_attention",
|
| 97 |
+
"deepseek_sparse_attention",
|
| 98 |
+
"linear_attention",
|
| 99 |
+
"linear_attention",
|
| 100 |
+
"linear_attention",
|
| 101 |
+
"deepseek_sparse_attention",
|
| 102 |
+
"linear_attention",
|
| 103 |
+
"linear_attention",
|
| 104 |
+
"linear_attention",
|
| 105 |
+
"deepseek_sparse_attention",
|
| 106 |
+
"linear_attention",
|
| 107 |
+
"linear_attention",
|
| 108 |
+
"linear_attention",
|
| 109 |
+
"deepseek_sparse_attention",
|
| 110 |
+
"linear_attention",
|
| 111 |
+
"linear_attention",
|
| 112 |
+
"linear_attention",
|
| 113 |
+
"deepseek_sparse_attention",
|
| 114 |
+
"linear_attention",
|
| 115 |
+
"linear_attention",
|
| 116 |
+
"linear_attention",
|
| 117 |
+
"deepseek_sparse_attention",
|
| 118 |
+
"linear_attention",
|
| 119 |
+
"linear_attention",
|
| 120 |
+
"linear_attention",
|
| 121 |
+
"deepseek_sparse_attention",
|
| 122 |
+
"linear_attention",
|
| 123 |
+
"linear_attention",
|
| 124 |
+
"linear_attention",
|
| 125 |
+
"deepseek_sparse_attention",
|
| 126 |
+
"linear_attention",
|
| 127 |
+
"linear_attention",
|
| 128 |
+
"linear_attention",
|
| 129 |
+
"deepseek_sparse_attention",
|
| 130 |
+
"linear_attention",
|
| 131 |
+
"linear_attention",
|
| 132 |
+
"linear_attention",
|
| 133 |
+
"deepseek_sparse_attention",
|
| 134 |
+
"linear_attention",
|
| 135 |
+
"linear_attention",
|
| 136 |
+
"linear_attention",
|
| 137 |
+
"deepseek_sparse_attention",
|
| 138 |
+
"linear_attention"
|
| 139 |
+
],
|
| 140 |
+
"linear_attn_config": {
|
| 141 |
+
"full_attn_layers": [
|
| 142 |
+
3,
|
| 143 |
+
7,
|
| 144 |
+
11,
|
| 145 |
+
15,
|
| 146 |
+
19,
|
| 147 |
+
23,
|
| 148 |
+
27,
|
| 149 |
+
31,
|
| 150 |
+
35,
|
| 151 |
+
39,
|
| 152 |
+
43
|
| 153 |
+
],
|
| 154 |
+
"gate_lower_bound": -5.0,
|
| 155 |
+
"head_dim": 128,
|
| 156 |
+
"kda_layers": [
|
| 157 |
+
0,
|
| 158 |
+
1,
|
| 159 |
+
2,
|
| 160 |
+
4,
|
| 161 |
+
5,
|
| 162 |
+
6,
|
| 163 |
+
8,
|
| 164 |
+
9,
|
| 165 |
+
10,
|
| 166 |
+
12,
|
| 167 |
+
13,
|
| 168 |
+
14,
|
| 169 |
+
16,
|
| 170 |
+
17,
|
| 171 |
+
18,
|
| 172 |
+
20,
|
| 173 |
+
21,
|
| 174 |
+
22,
|
| 175 |
+
24,
|
| 176 |
+
25,
|
| 177 |
+
26,
|
| 178 |
+
28,
|
| 179 |
+
29,
|
| 180 |
+
30,
|
| 181 |
+
32,
|
| 182 |
+
33,
|
| 183 |
+
34,
|
| 184 |
+
36,
|
| 185 |
+
37,
|
| 186 |
+
38,
|
| 187 |
+
40,
|
| 188 |
+
41,
|
| 189 |
+
42,
|
| 190 |
+
44
|
| 191 |
+
],
|
| 192 |
+
"num_heads": 64,
|
| 193 |
+
"short_conv_kernel_size": 4
|
| 194 |
+
},
|
| 195 |
+
"max_position_embeddings": 1048576,
|
| 196 |
+
"mhc": true,
|
| 197 |
+
"mla_use_nope": true,
|
| 198 |
+
"mlp_layer_types": [
|
| 199 |
+
"dense",
|
| 200 |
+
"dense",
|
| 201 |
+
"dense",
|
| 202 |
+
"sparse",
|
| 203 |
+
"sparse",
|
| 204 |
+
"sparse",
|
| 205 |
+
"sparse",
|
| 206 |
+
"sparse",
|
| 207 |
+
"sparse",
|
| 208 |
+
"sparse",
|
| 209 |
+
"sparse",
|
| 210 |
+
"sparse",
|
| 211 |
+
"sparse",
|
| 212 |
+
"sparse",
|
| 213 |
+
"sparse",
|
| 214 |
+
"sparse",
|
| 215 |
+
"sparse",
|
| 216 |
+
"sparse",
|
| 217 |
+
"sparse",
|
| 218 |
+
"sparse",
|
| 219 |
+
"sparse",
|
| 220 |
+
"sparse",
|
| 221 |
+
"sparse",
|
| 222 |
+
"sparse",
|
| 223 |
+
"sparse",
|
| 224 |
+
"sparse",
|
| 225 |
+
"sparse",
|
| 226 |
+
"sparse",
|
| 227 |
+
"sparse",
|
| 228 |
+
"sparse",
|
| 229 |
+
"sparse",
|
| 230 |
+
"sparse",
|
| 231 |
+
"sparse",
|
| 232 |
+
"sparse",
|
| 233 |
+
"sparse",
|
| 234 |
+
"sparse",
|
| 235 |
+
"sparse",
|
| 236 |
+
"sparse",
|
| 237 |
+
"sparse",
|
| 238 |
+
"sparse",
|
| 239 |
+
"sparse",
|
| 240 |
+
"sparse",
|
| 241 |
+
"sparse",
|
| 242 |
+
"sparse",
|
| 243 |
+
"sparse"
|
| 244 |
+
],
|
| 245 |
+
"model_type": "glm5_next_text",
|
| 246 |
+
"moe_intermediate_size": 2048,
|
| 247 |
+
"moe_router_dtype": "float32",
|
| 248 |
+
"n_group": 1,
|
| 249 |
+
"n_routed_experts": 288,
|
| 250 |
+
"n_shared_experts": 1,
|
| 251 |
+
"norm_topk_prob": true,
|
| 252 |
+
"num_attention_heads": 64,
|
| 253 |
+
"num_experts_per_tok": 8,
|
| 254 |
+
"num_hidden_layers": 45,
|
| 255 |
+
"num_key_value_heads": 64,
|
| 256 |
+
"num_nextn_predict_layers": 1,
|
| 257 |
+
"output_router_logits": false,
|
| 258 |
+
"pad_token_id": 154820,
|
| 259 |
+
"q_lora_rank": 1536,
|
| 260 |
+
"qk_head_dim": 256,
|
| 261 |
+
"qk_nope_head_dim": 256,
|
| 262 |
+
"qk_rope_head_dim": 0,
|
| 263 |
+
"rms_norm_eps": 1e-05,
|
| 264 |
+
"routed_scaling_factor": 2.5,
|
| 265 |
+
"router_aux_loss_coef": 0.001,
|
| 266 |
+
"scoring_func": "sigmoid",
|
| 267 |
+
"swiglu_limit": 10.0,
|
| 268 |
+
"tie_word_embeddings": false,
|
| 269 |
+
"topk_group": 1,
|
| 270 |
+
"topk_method": "noaux_tc",
|
| 271 |
+
"use_cache": true,
|
| 272 |
+
"v_head_dim": 256,
|
| 273 |
+
"vocab_size": 154880
|
| 274 |
+
},
|
| 275 |
+
"tie_word_embeddings": false,
|
| 276 |
+
"transformers_version": "5.16.0",
|
| 277 |
+
"video_end_token_id": 154833,
|
| 278 |
+
"video_start_token_id": 154832,
|
| 279 |
+
"video_token_id": 154855,
|
| 280 |
+
"vision_config": {
|
| 281 |
+
"attention_bias": true,
|
| 282 |
+
"attention_dropout": 0.0,
|
| 283 |
+
"depth": 24,
|
| 284 |
+
"hidden_act": "silu",
|
| 285 |
+
"hidden_size": 1024,
|
| 286 |
+
"image_size": 448,
|
| 287 |
+
"in_channels": 3,
|
| 288 |
+
"initializer_range": 0.02,
|
| 289 |
+
"intermediate_size": 4096,
|
| 290 |
+
"model_type": "glm5_next_vision",
|
| 291 |
+
"num_heads": 16,
|
| 292 |
+
"out_hidden_size": 4096,
|
| 293 |
+
"patch_size": 14,
|
| 294 |
+
"projection_intermediate_size": 10240,
|
| 295 |
+
"rms_norm_eps": 1e-05,
|
| 296 |
+
"spatial_merge_size": 2,
|
| 297 |
+
"swiglu_limit": 10.0,
|
| 298 |
+
"temporal_patch_size": 2
|
| 299 |
+
}
|
| 300 |
+
}
|
exl3-mcg-storage-abi.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"bits":4,"codec_family":"exl3-mcg","exllamav3":{"git_commit":"c5d9c657966ffeeaa9353f0cc899f18629da4a13","linear_storage_group":[["su","suh"],["sv","svh"],"trellis"],"module_key_rule":"official_weight_name_without_.weight","version":"0.0.43","written_suffixes":["trellis","suh","svh","mcg"]},"mcg_multiplier_hex":"0xCBAC1FED","output_tensor_count":150226,"output_tensor_names_sha256":"fef5367167148f0498c01da23584a68ab687dc9665668fc6f8c2f87e725b46eb","packed_reader_abi_sha256":"1990dbffd78f0866a8e75011c8276a55b784d2fe1886d1e6271d2173ed4f5e3d","plan_sha256":"a359003aea48137bdec97a0de50b5c9a31475a25d636ee7ca830e315a755f667","qualified_tp_sizes":[],"reason":"ExLlamaV3 v0.0.43 has no audited GLM-5.3 TP model load/inference receipt","receipt_sha256":"61a1becf7dfe3a9a0ab1579ed2b8157031cb74ae50d83762baedd93ce46b30c0","schema":"quant-pipeline.glm53-exl3-mcg-storage-abi.v1","serving_reader_qualified":false,"storage_checkpoint_verified":true}
|
generation_config.json
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"eos_token_id": [
|
| 4 |
+
154820,
|
| 5 |
+
154827,
|
| 6 |
+
154829
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 154820,
|
| 9 |
+
"temperature": 1.0,
|
| 10 |
+
"top_p": 0.95,
|
| 11 |
+
"transformers_version": "5.16.0"
|
| 12 |
+
}
|
materialization-receipt.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"auxiliary_files":[{"bytes":1570,"path":".gitattributes","sha256":"34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930"},{"bytes":1070,"path":"LICENSE","sha256":"30b85b6b9659f2e78aa259f8faf5d920a68dee7c9ced3fa6dba1f19f2bc4fca1"},{"bytes":7243,"path":"README.md","sha256":"3f0894b80aefb75c2afbaa0d4fb0f0f0993f14cb9700c6a5077ea6c201637b7b"},{"bytes":8617,"path":"chat_template.jinja","sha256":"41cff9af7b3a86c96751b107a8444f245fbda0bd5320b636a5bb1f7f4ba1a5c3"},{"bytes":194,"path":"generation_config.json","sha256":"230c30609ecbbb9e6583bedde8e7bdda0c6eb8fe5fad0eaeb3d1b293d751cb4f"},{"bytes":909,"path":"processor_config.json","sha256":"aae38374c94b08cc9b0547c6e64f05b951bd9735cea571c6988f5ed552bed3ed"},{"bytes":20217442,"path":"tokenizer.json","sha256":"19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d"},{"bytes":761,"path":"tokenizer_config.json","sha256":"98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc"}],"bits":4,"codec_family":"exl3-mcg","complete":true,"config_sha256":"4f5341e048984459471bfb9c894e6bf87e69b9c67402672af901631d1349f265","index_sha256":"2f64d21c67c90bbafeb36c4e9b2f06f54063ed439e9f7cf95962d425a1d8515d","main_and_mtp_complete":true,"mcg_multiplier_hex":"0xCBAC1FED","native_tensor_count":1618,"nonrouted_native_exact":true,"output_logical_bytes":175622979576,"output_root":"/workspace/models/brandonmusic/GLM-5.3-Flash-EXL3-MCG-K4","output_tensor_count":150226,"output_tensor_names_sha256":"fef5367167148f0498c01da23584a68ab687dc9665668fc6f8c2f87e725b46eb","packed_root":"/workspace/artifacts/encoding/glm53-uniform-k4-combined-public-mcg-v5","packed_tensor_count":148608,"plan_sha256":"a359003aea48137bdec97a0de50b5c9a31475a25d636ee7ca830e315a755f667","qualified_tp_sizes":[],"quantization_config_sha256":"1e5cdf56da1929c2a7d8982085324de05bb92144882792f034239e289316c645","reader_audit_required_before_publication_as_serving_ready":true,"receipt_sha256":"092be1ffa8db66bf02d4c370d0433a57aa48d4a6e5ce89723ef6a3bb7ca32643","routed_choice_count":37152,"schema":"quant-pipeline.glm53-k4-materialization-receipt.v1","serving_reader_qualified":false,"shard_receipt_sha256":["4787804e65f6a38192743871221cb261d921b49f469e57ee1ef48a4dd00c0ec8","a7dbc0557933d08a82f78eaa0999d4b6db7dff43648bca2ad4d8005194e9e4c7","7c4416e2292966356189316f300bbd916ac8b504ecedd5fda8fc94487cb31c42","7090dc000cc79bcf9acde98e02a6fe867616ff125e0b793666e0f4b558fb7d97","0048b2f986de44c06e1a36e3dac7f033bfeff197870f336cb89b5f001184ad23","8b3b12c9317dc58b5215840bf83dcf319acc89425d3c7b7d4884c69669b2e992","b8cc2a0ecc220b1db6fbfc1c0a1f492bd476b998e71e84b7f0bb643f761b3993","322929486e1aa3d267f1a729bf1f3ca5de1ee5ab0150c2bd24cc9b94f578b22b","3eef9071b220d0bcb7f242e6ac76913fc2f632f7bf3abd4e6f9a78d514c7199e","1495a8d87d8020d8950aa61937a6d359440e189959269e418872d19bf0a62574","029d978baaa5118cc747bb46f524960bb4082df1b5664ebd93074ae1f85d925f","2c247fec7b4301897c5c9752c3b1aabe4b8072ff5e8ba14542ac10bd4a98e4ab","ee2d0a53ada64d13973eab28f561c85b2726e91e775c49cef5f6a028acf6e60a","4ba411e782312e970e85b125c9bc518193e95ea82c9d4772fce9f9c152d14421","8e22e3d9dbd9141051b2f10bd6a6ddc9a5c9a6545d293026bd9d8493d07a6159","6bdc81be45ae5e8d3a11271ff6cbb8933d53cf5e348039136fa13d94953f1cda","cc37bb131007f0cbf888a9e6d704ec0671d9668f7877a1b8ec782d4eed949c36","949b26bc0dc1c7d774e1d94f195811ede5e83bea3dad35ee88e20c9cbd2b9481","08f7456700f1f3c66793fd35ed1ee49455185af70266c9336799bb97cb4d4868","4050087269b46f1ca40981db7c29a63175bcf2981877e0cb498da031852dee59","0aefbd2b99a5ea37111d44dcdfebf34a2c48aa52a83dbcf11a43501a7fbb3507","f6c9f6548e0c748d2ce36e2fcc4278937870b8791192e26f65419e7817eb4b67","1f44ae0d5ca977c26f943ddf0b37c642d1d0fe6221945cc047fde76c4ec191aa","cb78809169adc53477081c0e5905a1533215b1fca03140ed7b23424c700c08bf","24252cead52e9902571e8498f8c7b3e606780c8cc27830be9fcb11b472312ea7","2213db4ab7e6d9848f3786ef3610b072ad8ad7b92a63dfb4b6d889690c66d36f","ed6d0ce9cd105c2a8a1b96a6ed379bc65e4900bc8ea6c1ab413ef62ed4b98a25","8cd66aff75630f487e158670b30655beeb84406f79f0514da0dd1b929e137a90","83329b401cc271a6de111f335b0e3c408773cb48b6abf5d293ffa41231a5c3fd","fbbb9e5dddf2cf31343888fda7ae2f117bd4d9527a1e114d347b0760cef6c15b","83f6e8065b87f06645ca1d23ec0473ccd0ff88d03d8e973cd3ed2d48b1e2a8d4","75a143aafeb8eb753a5ead46e92a8a3d4db59094dc29849ebb1ff983d7e1381b","af5d4e94ff80f937c9374cea37ba822bcd4d530736cc3590dbb557bad591a736","3c46187a78e76b5a3b72c4647053b7c25e338b820a12d1f04418efd639e91f10","23f9bd936341fbb0cade55932e47ee767d606e0cac3a68817df1fe8f5eef823c","a1d85b7ef4e1dfce198f6e73008c3f075c1bdc67c01dd2a1978879a6d69057d7","bfc6ab445a8963df788cc84dc038ba1dc9e37049392a44d604812c2cfe8f92bb","293669e17cbd914c3381c855ab44e6d1346a10cf0e08d503a0bbcb38f1381f20","e14114776e1b40cc16e96251bfc69ef5ff890cc498dbe1d76ab94bc3a55d6273","b640d3ed6941e4d322f2fa1e017af2e1450179ef949ecbeddc40be658b34bd72","0a4fdd142a397cf7446261e9468fede1f5b31dfa64cf48bc98f2912e18eb755d","c2a9c33df1eaf4c0cf362b01d391219c7eb9dfd633a642cfa551788ec7fbbc99","5b3a04a9c86a74b22a6aff572b11a538b41d6f97398956195887558b363696ca","15ff0586aa5c36ca6aa6279278421709725338b244038fa9923f688293baa914","3040f35b06e8d851e203f78c61316c6f3b5b6029997a25787cc622fcfaedcc30","53d78880608675eab2e3bc16042cf6d1ab0427add3fb887355ad287cb43f05b8","69f0c085d88f368ac348f74f2617be4f2d177d2727876adde5127671817a5b39","fba7fb592cae5a572fd1021838d25634a0c0720efb99aaeb2af80f13bf984c5c","b7c3fac11fb62c654c48ca9d20286fc9b2565de0be61c5ed4347a8fc24414035","a03152227c05cda85f6655293859f68dd9bb204272147f193479c19bc6fb677b","6103818802b70da3d9183644f123b6e9bfb47ac35b87823ca67ad48bc76cd9a8","fc9263989931e8ca44befbc25b69ec979c782950537ab057a3b8530f9216f036","b6ce53dd03decfc5224fbafeccadddb1d89dec6fb79caca8dc10c660d41a5a82","7907c01fe69ffb702a47a739fd4359df7cc0ee060697f404f3aa66527b4ee23e","2896d01f4baad64b2e1da29a7c78c9e7eda6b9c91daa0822251de809a2837c53","48a02b15a1ebbda9e8aca7549e0789ceebbfd1eb1e2b26f9129b727a6144ebf3","18d3991e57cd0d53366c5c3fe5185af486ee82ef77e0e33ece41d1e5a88a2848","12abf7d8df3479520968a94fce034c5841f6743549d3ac112a1d32bcbf5370f4","2ab6b10483783c4dd4bc1fd91a79ae80ef9d0048f5498873f004966fc250d42e","6371fdf007009b1af4fec679e9dc096019869243ff83351cb60928988ebd4287","3afe08a341353ac88e7aab01de7ba168d172243e5fc4aedfa765523f5cadacbe","aa0cf5f9313b84597a189e3f3a81d63372578922aab482c47826fb6ec0ba0e61","1d094bee623e085669409a5da7c7bd8916fbf3699f968080ec76dcd0c300fab9","e54ed042eefbd416d6f2672e5f0fe82787a54e24aa8d3e512dadc9899ba4c25a","93965a481beb8985fbe0f795a65589c5dbbc63efddfcc7ddc20c9b4f0d0dc811","2cf328ad0e6cb80f6d52f9a06ae08916b6070e7154983ccb1dabb162ee6926b1","0112a1eaf180f995386a25a163e89246a510075eb91d1c0a560abdd60b7405c2","99f732ef15dcf65d3a12a47ae2c130ba806c49782cd90f28ba08301d210e4420","2890a532aa6ae7917d5233a8ef72310de3f96efb9d047779480c448ade8c3722","b5e083afdce3a9f831c0a123afa3ed5c67f256fcad17bc4d0b1392744b798d2b","40e2a53d6321664127850dd8e48dfa470a83e4a6142260789cc624bdab89bd83","34fad6d5ba6699be03880d1d55b5f15951e08e210d622e66397702668310d649","6ba951cad70d79b5bbe2878da2655608ce4d73140098de15ab587fe64783ebb9","fe3b1d1c6f1a3928f835e33f7df5bab6521fcee41e39bd7c2791995c02897ce3","0e5188901b8681a7a0a821471202bf050492775380b481a2d8f0d0e6784b2784","26c70f42035106c9b5d4840b5c0388136f5eb79febb605bb4db44d08ca7b6420","27a01fcc6e11b391bfc246c0687df400871a91db5865bb12ba8c4161aa1e724c","4586854147e388e94065c6dd97ba37e2bc2579b0ff84a570cd09080a86edd4dc","4702f974ebad0bd18c37b3e5d21c776d0c2453df777b4d654bf4ea01ec1a4a0e","23ca0316bd7af5b59b0c5dfdab3af3e3ae91482420055e247a7afe7c08f0784d","e0609d0ece0ac8e924b4577dd2f67797032b602d14af5ffc788a7efe04b74e43","4cc3e0090aa06531345abf6391a031857e01792ea539224c9644fb1ae83e3880","8cc8bca46505d6154bb9d554e0a690142ef6725b1b64f07549540a7ac89194b7","11fb79267e96570421fb6e1b285e6cbecc7537e13c90ae88becf023bf555e77c","3d6c0d72dec5dcbeab6965a224f36fb4bcff4cc0eac909ac89f1f065bd1219ee","7125333bccfb6ae064607cb11a94e22737ba2a61bc30ba13bf8037177301c816","11f449ddcbe090008ca68ab6b417bd1387facb575e70ecf39804ca055e59a4b5","170f42e3bdcd09ce1d27f8d30519e9de927952f5ad7f0abd3947b7796bf2b3bd","2ba46cc5c55b9113804a4e877b977f240383974113ea8e68b868adf31c1d1beb","064e87c3e515aa0cf94e3d1e23f4dae689d259be0b176163fd5622549835e7b4","e130a99def7b9d4162175ae5e71984f381ea21aff5ae9801c3c650971d00d633","fdfd5588ee4b7146412a60264956571e20bef187be4e40abc96371ed15d62b0e","50a79b77c990ed37dd1beccdec280f92b20717b15c0079296de3811e688a0d40","d4850f80cabc7d6b0c49e0e3065c0f8b28da5764ce030a780c5e607fb6d4c1dd","e8c07a16e85f696813f037cdb5aa4fbdc3b66ea575dc229df1dfa47547f0c5f3","908e9aee7982368aaf0b1bf7761dac6a5632a9291029addfa11361cab3774dfe","3ea2ec13a518b10dd2abb51fe00c7af587a156ccffc8dbf6a70533ab64fa09cf","ac98dc64e1a22c32501652ed0b656ce8073575eac0778bb05f979b1e2eb13d71","151e00e4b71a20eda909ef0ca79949fdbaf4507e82eb918cf0ed9de3aee58207","5c5db54c5924f05f5add0e0bd1f88562146dafd205e9cb79c7dab66cffe7fcca","8bc176b56a91fcb185091162907ebe33fc11c335b5ce12456098a5917b6b9091","ecfd63ccccb9b34df5943885c7359c7b52890c4c1dfbbed1f3f75a1db522f7c5","19d389460e87bde33e5e56daee4d5baa33d3c8a564e9976b95ff7734c59dfc17","8392c4ed39802e105a6f685c2b52fe29bb8f8bda5b92a6587032ed311b588807","a16e42b81a36f37677e27a5a019b35a9bf225d3a5f670fc5e8e9a91c64d7238f","acb886c63c60a2b85de6888fdbbd7ad2d9e9252e6b9608dabddc9091a64b82fc","f62613fe3a04aaa58c03c6c9383fefe745809dc0cfc03b31cad0f943fca00b56","ad6b5a04f51c7ab522cba76bfbc6159524e5d8cb9ea481182a548bd5da06840b","a251d976edb49d2adb907ac2e3a7ec18f498f04b81a8e10ee0e5706a56b2d9b8","5e607d8d8fcce44dc0d8e2676cb535706207f04be6608769982f1f4e3e7f76d4","7ca5ecbc3a2b1a79443e265556bd5c720d822261d1b22c5f9da3f9320f752329","c899cca8f6a22c68bae5038080e1259461b7a649c2ee45896c1a67ecd27b80a8","d8a4d5b8bf1f0fe7946bafc3d1f1ad3fdf43f8cb194c4433c803a51b6c90f8a4","128b7de52085adef693f7a61013b8058e9417528526ad4ad326e2353bf0c7c0c","41ef3ba0018fe24b7ac4339e7e6d2f385a686a5837e2a8bb5591afb3d8c08289","bf80d5253e721b901bccd669eae419571c63a03314629704c49b44dce522bf00","78431f17886ceead18c8dd83a874344b785a691496798368ca2ea3e3f31c1445","af21cef91533d3621272db66338a9dec37adf2a4459981fed71fccd353ddd3a9","5f5114a8a85c37bbe09de2a1dd539f3a8fc115abeb86a2cb259f0cbff26bf72d","1c2356dec67a55f9c024cfabbb54eab772a0b91c6d9c11284a0697099a625a5c"],"shard_sha256":{"model-00001-of-00120.safetensors":"46cc9e99897500fee92f2f08c95665cda51a704f835548465a1023cd4db63ef7","model-00002-of-00120.safetensors":"b78eebdbee138150d310d7c655baae1ea31720b8ad53f39389b1679ce0f51552","model-00003-of-00120.safetensors":"d377c4b645e7283f21370a1d12d90f238ec38194f78ddc483aea6be45c0db2c6","model-00004-of-00120.safetensors":"65f841ba0f3449b18af49eca276db6047a78088a3ff293f9edd41dcb4082af65","model-00005-of-00120.safetensors":"d0425becf2a42244527553a983b269e2656521069c6e9467d0ce6912dd2fee80","model-00006-of-00120.safetensors":"3f6ded63fe3fe96a176c1ffad431c2e7350307238a89fb89ac3947605ea4d72a","model-00007-of-00120.safetensors":"a8e2cf2d869afb825934ad43bcbbf7e16132bdfca8443ca52a39c46b36f775cd","model-00008-of-00120.safetensors":"eaaf3e209d6bcd37efae31e10fbbc822755629a701076ad9a527d80c216810d5","model-00009-of-00120.safetensors":"ad30bcac854cc303563eb2f280439bf86d7b56c81a43e00175fecd55d63f9905","model-00010-of-00120.safetensors":"35530edfbce782687f3b06b85f85f34decc537a53015c0bee1a4caf82c7cc78b","model-00011-of-00120.safetensors":"686f13c8151614b4d85c02c20e8a786bdfa1eac82d8260bd8d877833db346288","model-00012-of-00120.safetensors":"79a19881d8a37ac0afbcd0b0e77ea45150900467b43ae0b412288ce82154a718","model-00013-of-00120.safetensors":"d07940d68fc7b90f99eca406819e4bcd94995a909e32f0f3d15f3251c8f06552","model-00014-of-00120.safetensors":"ca3290268d136656a2b8eb56328fcf275824bcd60c269a39d9b1ee02d29d42fd","model-00015-of-00120.safetensors":"2172d6d1c76e4d79d0d6147e693080a1ee2fd2761518764ea7ae42d6e21816ca","model-00016-of-00120.safetensors":"b33392a8b5f66e82115d1c7bbe830945fdda9b5b3a2d5094c9e8f7120186d09f","model-00017-of-00120.safetensors":"b198f9d0606953932f131b3d1124488702280ca55627d5c9156823a87a56144b","model-00018-of-00120.safetensors":"b98e6fa60d19eeabe5f937dfa5cde62ee0cffb7e8687f7c6e8e54be797e76cfd","model-00019-of-00120.safetensors":"1ac4f98e4a16f9f620d83a51353a5b3ae4af05055b7777633981a930f063a9a4","model-00020-of-00120.safetensors":"db5f5e4bfe9704b55cad6fddcbeb0b50c0f0d760af247f13ea78e6b9a13bf5e9","model-00021-of-00120.safetensors":"94abdb9997af61ed92f43d58c73c96947add0ac1580c2255eaba2b254aa34782","model-00022-of-00120.safetensors":"d1c09ce5b40bf2f976a6918188b10382735be8f9d936fc7d9e3d174d67fcb193","model-00023-of-00120.safetensors":"e75296748bb0940bf8a45e3743444d46404a0984c25164212764211d07ea5c3a","model-00024-of-00120.safetensors":"f9fa4d4312b74a757befe4f488dab56370a9c21771a86c2b92e3ac74ed068863","model-00025-of-00120.safetensors":"6f94c10adb857a39f5535be2b50b8037d08c798f13df4eb934fb9c14d44e9962","model-00026-of-00120.safetensors":"0c65f0517b61444c2b9c243ed5a97a6b2aec76054da36c2ef23b7d0263d0f1f4","model-00027-of-00120.safetensors":"af324d2631116bb4ca61b15578ac270874ce82035cb262e1adcb4056a76841b0","model-00028-of-00120.safetensors":"5e031de67c3c86ca62170a72c996c1cbd588a3a36b5a026dd0823e088793f196","model-00029-of-00120.safetensors":"bae837ec87eca4b857597783fc9035d4fa6e049addd4a1f46c052de1a31bb6da","model-00030-of-00120.safetensors":"bbe67349b26057fe942bfce9fdff6422a9b822924b2445bbde7c2ca4c974c707","model-00031-of-00120.safetensors":"3aebbcb6ef80e74313e015c5ce828bb09fb02d65256e2567bf6bee2d1bdcf887","model-00032-of-00120.safetensors":"cb9063b4790ac8ede704b801aabf416adfbcc2cb4c3cece85d499ffd710b5f00","model-00033-of-00120.safetensors":"598e794cb00fcb85f20ff6e701e5a98a601b19681d77c11c370081b8836d0417","model-00034-of-00120.safetensors":"fe68ce83f4b32da0cdc5f6803ae4ac977f2e0b007df627dce80ac93b3fd88817","model-00035-of-00120.safetensors":"5c439c150741aa601c9f8f8a8f0bb650a59deb9d66e018e3e003baf97551a0e2","model-00036-of-00120.safetensors":"bba2d7a1df63672dd6400c74bd07352698bc9e74497812963d76f6c379a4fcb7","model-00037-of-00120.safetensors":"39ac664b9906d0970fedd207c20fe8b54cd16703ba765db4de332f2f3a4ae24f","model-00038-of-00120.safetensors":"26b70b8b61940befb31f4bf5956dfd9dc89500bddb36b4e79eb046ab39000412","model-00039-of-00120.safetensors":"c2599dd1cdc04c23c3383ecccad7d50d11d0e52c6602574bc1da2428e84f7028","model-00040-of-00120.safetensors":"e109dbf65aba7827809cc67334065364a1684715179b31311e0a31c00d8dd48c","model-00041-of-00120.safetensors":"bb85b908911687c0e276e43e81b5a4a265a87c00965324532681097ca491bc9f","model-00042-of-00120.safetensors":"a017885e30064729e5f70cbaeda434663c6c382aa9d2ba2a73f820a8cd5b6bbe","model-00043-of-00120.safetensors":"04f3aeccb23c779ac9e6be9e952418b95cac19327e7873dce5f69e4ad728f6e0","model-00044-of-00120.safetensors":"fa5a385f7a8220ed99bc081f2fe26a07c480a3da7f497be57cf95f96ceb1c34c","model-00045-of-00120.safetensors":"b0b508dbd6217fe861a68bd8890415fc0f97404ed9ea7155adae9476b12c27d0","model-00046-of-00120.safetensors":"c0e27a8d0a9e4521e96f17ad0e40de8152ddd8c26f7739520cc052069e4432f7","model-00047-of-00120.safetensors":"5c471e0726598af910a1d5c895eca9bf41afe1e45311ee0dc962572b21097f2d","model-00048-of-00120.safetensors":"1d957114322b3d9dbb4cca73d9bba9cf628ddc7f5a6dd1d6af6224eb6aff003e","model-00049-of-00120.safetensors":"895d22c61a559667d84ac65dcd3012de18103738d3660c0c84db0b90e29c7e32","model-00050-of-00120.safetensors":"c2bd099b71e35b8af36785f23e025c8b3ce393197b801cd3e53ee33e9be71531","model-00051-of-00120.safetensors":"1c30583b2edd80ed55db4086e1b846f7a9de54f7996bfa07e2ac0039f702c10a","model-00052-of-00120.safetensors":"7c800f2f476cff08606cc3b036571d43ea501518fcba658aeda5db0fce815346","model-00053-of-00120.safetensors":"43acf4bbb16dda6f5a7c31240108bad19c64ac5eb9d20022321c4eeea48bc991","model-00054-of-00120.safetensors":"98bb70809af0d4d3177a377eefcbf728dd7fbd9e3891f130c05c66d29cd04860","model-00055-of-00120.safetensors":"285c605b3391fbca1a40f28fdb6c1856a155ccfe6de43eb59e11033360dadf51","model-00056-of-00120.safetensors":"6f825c25cd071930e7cbec832fe71a65767024b6853bdf554a9dd6534d0cd25c","model-00057-of-00120.safetensors":"9c7e6d5fd6d4138fde51eef51a83ff8a33424294c44d52d1b62b42ddb79ada4d","model-00058-of-00120.safetensors":"0b73d4dc1f373046f08a000d60484a7cf5335c4b0a1e4588d0cf8089f3ed75f2","model-00059-of-00120.safetensors":"c5e83b54c3f1912aa5fd35f1c3170415f54708887f6f358cb1eac0b93391394d","model-00060-of-00120.safetensors":"c37b7e82d16a82243664ecac9e417a815195e1c26d9affe2ce681bffcb2fa046","model-00061-of-00120.safetensors":"ec26e65bf26f2dadf4e4c7aeefe48acbd9b677a3d03ba0d3229f1cc796647bb4","model-00062-of-00120.safetensors":"98c6427e9b22529ea985aa5d343e24055d3fd570b866e4d8dfb8f0b9470caedb","model-00063-of-00120.safetensors":"dacdd78b8a4f74ea5502d28b0a8d0c632a05252e095abc6245f0a63bdbd57a46","model-00064-of-00120.safetensors":"7f134d623d5f229cb4732905ceb269404e1b00a72b8c25cc4f09d1e8610aabc5","model-00065-of-00120.safetensors":"16661291a3d218f765edcb3831ff9426af21d6d56e6d8082b194e5cac9348109","model-00066-of-00120.safetensors":"e8c5392e733bd98d5d9f38d47e18cf6834bcbc0b8d24937c42d36ebc60604910","model-00067-of-00120.safetensors":"f2fb7876facab2bbb569fe94ab41dd1e71c50a3811853bf37688e5ebdc5813c0","model-00068-of-00120.safetensors":"abc3009f10522e97eedbcd34903e3aafa55f7d72efa4ff3db4b5c4fb28259ca1","model-00069-of-00120.safetensors":"ee4f3cb631ca5b0552fab89a3ef05ed0d8dab5ee0066fa245af479c962071e53","model-00070-of-00120.safetensors":"8b60971fe302e1bcf1bb7da900276a3f26fcd38b10d6760330ee59ea8df7a589","model-00071-of-00120.safetensors":"9fb9f3581b0a09d43e94e0476b9fa0971b3ab30b3d980bf6bb0814fb92db82d4","model-00072-of-00120.safetensors":"caec81374ed646f0d7953f81565438ce18dfc11197cbb1a442b557cfb907dd23","model-00073-of-00120.safetensors":"313e11a85717e5fe56c6901f16c10aab760619b7655d355185a17dcb6986397a","model-00074-of-00120.safetensors":"3c22a6012bb34c264869fd8a93ac019175f22d9d255cdb1987d25572f6ebcb37","model-00075-of-00120.safetensors":"55992a5b643dad0a06c6bd8e4a13bac8980d31f3e773d56cc86a0c36543c02e5","model-00076-of-00120.safetensors":"3dea15fd98ef8c45a23876a3dd1f3f6c06feb6c5e3a35b29dde93a82196e9f63","model-00077-of-00120.safetensors":"fb6efae4033a54282fa0f779ce41dd64edff903621e829e23c28f2423f032059","model-00078-of-00120.safetensors":"1f231a1c8149307a14c940df734637e03b0cad5db40e4165c69b9a5e817471d9","model-00079-of-00120.safetensors":"84eb7df2440c3fe0cad633bfdb05fc1f9f4a69514d33a3c48e08f751e8729b03","model-00080-of-00120.safetensors":"1008d2f310da3804fe85ca18c86f7f40c7e7005afe3edd0e17bff9207edb15cd","model-00081-of-00120.safetensors":"30bb97e87009412ec4445773ab5dde2267f395290a37ea204b0b94fb78b06c16","model-00082-of-00120.safetensors":"6314d366c0a954e7a8beed9ead7389dc5c9ec6a78667f63778edb68a1ec5a5a7","model-00083-of-00120.safetensors":"424127fa0a70e70d5073200c3ede1097dc31345b67309e9c1ca14fc1d2c601c9","model-00084-of-00120.safetensors":"8f5be1a45eeafd520fa3e0adceffb15cceaa6510c61d032475f017966885c248","model-00085-of-00120.safetensors":"22722c615db28a151620c8a6d11714f6de26bc386f3d495a89666191122ee1bf","model-00086-of-00120.safetensors":"dcd025dc9e48dd3744996a3b9d337d3c8636c46591e80ab6cd489aac78b61b76","model-00087-of-00120.safetensors":"c00bdc2031dd9c042fcf56613979d029256c2c337c6dddf61f6e5efe18e6c883","model-00088-of-00120.safetensors":"147abc2a72e6eea5941cd03175febb962a81d068a2872c6594bef6f3e5535007","model-00089-of-00120.safetensors":"5d2ec3fd271c7a2ec4161f4e5bf2c14eef7f549ae1a6d39bb666614e26d2dfc1","model-00090-of-00120.safetensors":"2bb2e9ab785905faaeeb8b6a3df7ff2f7ae246fb1e3f51fd4cfd0b3af94e44f1","model-00091-of-00120.safetensors":"8ccbc08c9dbcd37d1ec6cfd772667b12cd521bfd4dc05113e7e9f0ce1d2b9603","model-00092-of-00120.safetensors":"20422d9ea7e0997852897daf8e6c84b420c9c8b3a700b301f416091cdb8e4efb","model-00093-of-00120.safetensors":"753329dbb8d2b4efc7cf4ca7fe9b1455e0128cbe725a98fc91a05fcf38d616d9","model-00094-of-00120.safetensors":"f08e09cb29e5c1ffc05b128d581e815914ced8f30949a1e2e114470dbbba2dbd","model-00095-of-00120.safetensors":"c08536722ef0a2cecb5a5d843fd0086275c329918eff733676af1cb6bdb99abb","model-00096-of-00120.safetensors":"fafe3a925e795c9b570c8eefeb0ea492df97e941f23e2e7e8206ded8cd28933d","model-00097-of-00120.safetensors":"e616de880c9587930ecf502799255189c0f03e102273ade47dbc104c136e92c5","model-00098-of-00120.safetensors":"7c34ac45ffba12a049c0b563cd002a50d1fb6ea9dc80c6c23203c43d5768705c","model-00099-of-00120.safetensors":"9693db794a7992532a96bd0ea185a49a76c123fb50f7bcf4fff0902847e0c6ed","model-00100-of-00120.safetensors":"a19fb303a9814d5082fc3fd5d69127bc6724bf574276e6d18918b56ab46e8eee","model-00101-of-00120.safetensors":"c24ca6ae7ce061eba28d302b878700d30ec22656a69a3db53bbb4ac4ad1ae961","model-00102-of-00120.safetensors":"b5c8396800acefb6d0bc7a0a48cd055d28faa94dc2bb34f1867a58ec83991028","model-00103-of-00120.safetensors":"09139292ba5af27e66767a6860a53b7693f091c513bac154c32d44b408783738","model-00104-of-00120.safetensors":"b0af3c5b38f078c72d405319fb47ca4c7ef74324266106ca3962ae63dc6e0a28","model-00105-of-00120.safetensors":"f475efcce530b46749e9ece468b65a5aa65a073b7fcec00c22aec5b02e4e7782","model-00106-of-00120.safetensors":"1059fe4a53c233b1fdb28ae431d61190c6491f55223c58c0660dfead53be7bc9","model-00107-of-00120.safetensors":"57e1f99044388f5c0e5b422d678e9477475dc170dcc97b654f931e3ef9facfee","model-00108-of-00120.safetensors":"286de5d27896da595638cc5aae2e390bd7676e0d450f490842c165975febff5f","model-00109-of-00120.safetensors":"b41955ae5e684cff92ae005d27b626b2abbe72fc1ad0e9b273b6d71ca3a74b66","model-00110-of-00120.safetensors":"b2bd23f9b6d11910f9b7f9e4951ae254bc64c1844a2541de48cd4ac6755d0abc","model-00111-of-00120.safetensors":"091e1d588b899c5369b713a053a7bc76ad35bc93a81af20961167f89bc953b1f","model-00112-of-00120.safetensors":"8a7c2486aa93f1c952da2ffd6137f2f38b443e21923c5f640edcaab6c340d4a1","model-00113-of-00120.safetensors":"8f3205a5dd7e9162c2ec89e19b82c10fcdd41f716b101322833f33cc27aed753","model-00114-of-00120.safetensors":"e3cf6f9ea4e07df98a3bc4cbac85f9228ff3e9fd45f02f8735672d27f81a9404","model-00115-of-00120.safetensors":"347b2e08fca7a5bbe63e39e2bd225fb0af5a7ff318120cf7e5289c4ee2a72c23","model-00116-of-00120.safetensors":"3d9c0120686a8db80f7c6bd5c1044e737a4a8f5dc8f4111038308aa0cf80d88f","model-00117-of-00120.safetensors":"48eca9375a20c8f8c6435ea6e4ee709238680873967408dc0ba499179e7b7da5","model-00118-of-00120.safetensors":"f7df8825ffec03ac813ecfabc62f9a416c8884d11aa35da655f322a6b6870817","model-00119-of-00120.safetensors":"8e393cfce320eba98c19f69574725ab1aa5153d710eaa651df5e2c35bfeca1ae","model-00120-of-00120.safetensors":"15ffa59ac9a36b7f0fbfabdea893dfc6b6db4dd921d7a291b0056062d6937dda"},"shards":["model-00001-of-00120.safetensors","model-00002-of-00120.safetensors","model-00003-of-00120.safetensors","model-00004-of-00120.safetensors","model-00005-of-00120.safetensors","model-00006-of-00120.safetensors","model-00007-of-00120.safetensors","model-00008-of-00120.safetensors","model-00009-of-00120.safetensors","model-00010-of-00120.safetensors","model-00011-of-00120.safetensors","model-00012-of-00120.safetensors","model-00013-of-00120.safetensors","model-00014-of-00120.safetensors","model-00015-of-00120.safetensors","model-00016-of-00120.safetensors","model-00017-of-00120.safetensors","model-00018-of-00120.safetensors","model-00019-of-00120.safetensors","model-00020-of-00120.safetensors","model-00021-of-00120.safetensors","model-00022-of-00120.safetensors","model-00023-of-00120.safetensors","model-00024-of-00120.safetensors","model-00025-of-00120.safetensors","model-00026-of-00120.safetensors","model-00027-of-00120.safetensors","model-00028-of-00120.safetensors","model-00029-of-00120.safetensors","model-00030-of-00120.safetensors","model-00031-of-00120.safetensors","model-00032-of-00120.safetensors","model-00033-of-00120.safetensors","model-00034-of-00120.safetensors","model-00035-of-00120.safetensors","model-00036-of-00120.safetensors","model-00037-of-00120.safetensors","model-00038-of-00120.safetensors","model-00039-of-00120.safetensors","model-00040-of-00120.safetensors","model-00041-of-00120.safetensors","model-00042-of-00120.safetensors","model-00043-of-00120.safetensors","model-00044-of-00120.safetensors","model-00045-of-00120.safetensors","model-00046-of-00120.safetensors","model-00047-of-00120.safetensors","model-00048-of-00120.safetensors","model-00049-of-00120.safetensors","model-00050-of-00120.safetensors","model-00051-of-00120.safetensors","model-00052-of-00120.safetensors","model-00053-of-00120.safetensors","model-00054-of-00120.safetensors","model-00055-of-00120.safetensors","model-00056-of-00120.safetensors","model-00057-of-00120.safetensors","model-00058-of-00120.safetensors","model-00059-of-00120.safetensors","model-00060-of-00120.safetensors","model-00061-of-00120.safetensors","model-00062-of-00120.safetensors","model-00063-of-00120.safetensors","model-00064-of-00120.safetensors","model-00065-of-00120.safetensors","model-00066-of-00120.safetensors","model-00067-of-00120.safetensors","model-00068-of-00120.safetensors","model-00069-of-00120.safetensors","model-00070-of-00120.safetensors","model-00071-of-00120.safetensors","model-00072-of-00120.safetensors","model-00073-of-00120.safetensors","model-00074-of-00120.safetensors","model-00075-of-00120.safetensors","model-00076-of-00120.safetensors","model-00077-of-00120.safetensors","model-00078-of-00120.safetensors","model-00079-of-00120.safetensors","model-00080-of-00120.safetensors","model-00081-of-00120.safetensors","model-00082-of-00120.safetensors","model-00083-of-00120.safetensors","model-00084-of-00120.safetensors","model-00085-of-00120.safetensors","model-00086-of-00120.safetensors","model-00087-of-00120.safetensors","model-00088-of-00120.safetensors","model-00089-of-00120.safetensors","model-00090-of-00120.safetensors","model-00091-of-00120.safetensors","model-00092-of-00120.safetensors","model-00093-of-00120.safetensors","model-00094-of-00120.safetensors","model-00095-of-00120.safetensors","model-00096-of-00120.safetensors","model-00097-of-00120.safetensors","model-00098-of-00120.safetensors","model-00099-of-00120.safetensors","model-00100-of-00120.safetensors","model-00101-of-00120.safetensors","model-00102-of-00120.safetensors","model-00103-of-00120.safetensors","model-00104-of-00120.safetensors","model-00105-of-00120.safetensors","model-00106-of-00120.safetensors","model-00107-of-00120.safetensors","model-00108-of-00120.safetensors","model-00109-of-00120.safetensors","model-00110-of-00120.safetensors","model-00111-of-00120.safetensors","model-00112-of-00120.safetensors","model-00113-of-00120.safetensors","model-00114-of-00120.safetensors","model-00115-of-00120.safetensors","model-00116-of-00120.safetensors","model-00117-of-00120.safetensors","model-00118-of-00120.safetensors","model-00119-of-00120.safetensors","model-00120-of-00120.safetensors"],"source_inventory_sha256":"f56e9d6250e2d108f8307322f033e53c0ff26d5b2688ebf12b891c26439fea44","source_model_revision":"a6c167b62691b2bac901344b65cb651a70f53e43","source_tensor_count":38770,"storage_abi_receipt_sha256":"61a1becf7dfe3a9a0ab1579ed2b8157031cb74ae50d83762baedd93ce46b30c0"}
|
processor_config.json
ADDED
|
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"image_processor": {
|
| 3 |
+
"do_rescale": true,
|
| 4 |
+
"patch_expand_factor": 1,
|
| 5 |
+
"merge_size": 2,
|
| 6 |
+
"image_mean": [
|
| 7 |
+
0.48145466,
|
| 8 |
+
0.4578275,
|
| 9 |
+
0.40821073
|
| 10 |
+
],
|
| 11 |
+
"image_std": [
|
| 12 |
+
0.26862954,
|
| 13 |
+
0.26130258,
|
| 14 |
+
0.27577711
|
| 15 |
+
],
|
| 16 |
+
"temporal_patch_size": 2,
|
| 17 |
+
"patch_size": 14,
|
| 18 |
+
"min_image_tokens": 16,
|
| 19 |
+
"max_image_tokens": 8000,
|
| 20 |
+
"image_processor_type": "Glm5NextImageProcessor"
|
| 21 |
+
},
|
| 22 |
+
"video_processor": {
|
| 23 |
+
"do_rescale": true,
|
| 24 |
+
"video_processor_type": "Glm5NextVideoProcessor",
|
| 25 |
+
"patch_expand_factor": 1,
|
| 26 |
+
"merge_size": 2,
|
| 27 |
+
"image_mean": [
|
| 28 |
+
0.48145466,
|
| 29 |
+
0.4578275,
|
| 30 |
+
0.40821073
|
| 31 |
+
],
|
| 32 |
+
"image_std": [
|
| 33 |
+
0.26862954,
|
| 34 |
+
0.26130258,
|
| 35 |
+
0.27577711
|
| 36 |
+
],
|
| 37 |
+
"temporal_patch_size": 2,
|
| 38 |
+
"patch_size": 14,
|
| 39 |
+
"min_image_tokens": 16,
|
| 40 |
+
"max_image_tokens": 240000,
|
| 41 |
+
"fps": 2
|
| 42 |
+
},
|
| 43 |
+
"processor_class": "Glm5NextProcessor"
|
| 44 |
+
}
|
provenance/source-model-revision.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"example_only":false,"inventory_sha256":"f56e9d6250e2d108f8307322f033e53c0ff26d5b2688ebf12b891c26439fea44","model_id":"zai-org/GLM-5.3-Flash-BF16","receipt_sha256":"bba6e387dd056f69c144dd0fc6c61ff44cf0d447bed3ffe88091e11390a626dd","revision":"a6c167b62691b2bac901344b65cb651a70f53e43","schema":"quant-pipeline.glm53-source-model.v1","weight_dtype":"bfloat16"}
|
quantization/recipe.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"candidate_rate_grid_invoked":false,"codec":"EXL3/TR3 MCG","example_only":false,"global_allocator_invoked":false,"nonrouted_policy":"native","profile":"k4","routed_expert_bits":4,"schema":"quant-pipeline.glm53-uniform-quant-recipe.v1","source_model_id":"zai-org/GLM-5.3-Flash-BF16","source_revision":"a6c167b62691b2bac901344b65cb651a70f53e43","target_tensor_parallel":2,"tensor_policy":"uniform-routed-experts"}
|
receipts/checkpoint.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"example_only":false,"five_run_kld_receipt_sha256":"df3d2388967f3ff578bd5c92e11875c4198d0337c25aa98eeb328271c0034ba3","materialization_receipt_sha256":"092be1ffa8db66bf02d4c370d0433a57aa48d4a6e5ce89723ef6a3bb7ca32643","native_copy_receipt_sha256":"fede3330cb1c8635df9174435a9de492f2d6a7416e258910d40978954107bb51","packed_kld_receipt_sha256":"ce48cf6d3adc69da40d06422789f98a61c400bb9cda726c64e0851df792bedd6","profile":"k4","qualified":true,"receipt_sha256":"1120467d01f99cd890c055ed74460a955e32c174b5ece7fadb495eb5d142158d","schema":"quant-pipeline.glm53-k4-publication-checkpoint-gate.v1","source_revision":"a6c167b62691b2bac901344b65cb651a70f53e43","tp2_runtime_receipt_sha256":"a4631352b8fe705e9c6106ea51bb08325be6019c186e7d148306c1df7aab3951"}
|
runtime/src/quant_pipeline/scoring/blend.py
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from __future__ import annotations
|
| 2 |
+
|
| 3 |
+
from collections.abc import Callable, Sequence
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class BlendedModule:
|
| 7 |
+
"""Factory for a torch module that blends BF16 and candidate outputs."""
|
| 8 |
+
|
| 9 |
+
@staticmethod
|
| 10 |
+
def wrap(source, candidate, alpha):
|
| 11 |
+
import torch
|
| 12 |
+
|
| 13 |
+
class _Blend(torch.nn.Module):
|
| 14 |
+
def __init__(self):
|
| 15 |
+
super().__init__()
|
| 16 |
+
self.source = source
|
| 17 |
+
self.candidate = candidate
|
| 18 |
+
self.alpha = alpha
|
| 19 |
+
|
| 20 |
+
def forward(self, *args, **kwargs):
|
| 21 |
+
source_output = self.source(*args, **kwargs)
|
| 22 |
+
candidate_output = self.candidate(*args, **kwargs)
|
| 23 |
+
if not isinstance(source_output, torch.Tensor) or not isinstance(candidate_output, torch.Tensor):
|
| 24 |
+
raise TypeError("blended modules must return a tensor")
|
| 25 |
+
blend = self.alpha.to(device=source_output.device, dtype=source_output.dtype)
|
| 26 |
+
return source_output + blend * (candidate_output - source_output)
|
| 27 |
+
|
| 28 |
+
return _Blend()
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def module_path_attribution(
|
| 32 |
+
model,
|
| 33 |
+
module_pairs: Sequence[tuple[object, str, object]],
|
| 34 |
+
loss_for_batch: Callable[[object], object],
|
| 35 |
+
batches: Sequence[object],
|
| 36 |
+
path_nodes: int,
|
| 37 |
+
):
|
| 38 |
+
"""Run simultaneous layer/module Aumann-Shapley attribution.
|
| 39 |
+
|
| 40 |
+
module_pairs contains (parent_module, attribute_name, candidate_module).
|
| 41 |
+
The source module is restored even on failure. Parameters should be frozen;
|
| 42 |
+
only one scalar alpha per unit receives gradients.
|
| 43 |
+
"""
|
| 44 |
+
import numpy as np
|
| 45 |
+
import torch
|
| 46 |
+
|
| 47 |
+
nodes, quadrature = np.polynomial.legendre.leggauss(path_nodes)
|
| 48 |
+
nodes = (nodes + 1.0) / 2.0
|
| 49 |
+
quadrature = quadrature / 2.0
|
| 50 |
+
alphas = [torch.nn.Parameter(torch.tensor(0.0, device=next(model.parameters()).device)) for _ in module_pairs]
|
| 51 |
+
sources = []
|
| 52 |
+
for (parent, name, candidate), alpha in zip(module_pairs, alphas, strict=True):
|
| 53 |
+
source = getattr(parent, name)
|
| 54 |
+
sources.append(source)
|
| 55 |
+
setattr(parent, name, BlendedModule.wrap(source, candidate, alpha))
|
| 56 |
+
attribution = torch.zeros(len(alphas), dtype=torch.float64)
|
| 57 |
+
try:
|
| 58 |
+
for node, weight in zip(nodes, quadrature, strict=True):
|
| 59 |
+
for alpha in alphas:
|
| 60 |
+
alpha.data.fill_(float(node))
|
| 61 |
+
alpha.grad = None
|
| 62 |
+
for batch in batches:
|
| 63 |
+
loss = loss_for_batch(batch)
|
| 64 |
+
gradients = torch.autograd.grad(loss, alphas)
|
| 65 |
+
attribution += float(weight) * torch.tensor([float(g.detach().cpu()) for g in gradients], dtype=torch.float64) / len(batches)
|
| 66 |
+
finally:
|
| 67 |
+
for (parent, name, _), source in zip(module_pairs, sources, strict=True):
|
| 68 |
+
setattr(parent, name, source)
|
| 69 |
+
return attribution.numpy()
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"clean_up_tokenization_spaces": false,
|
| 4 |
+
"do_lower_case": false,
|
| 5 |
+
"eos_token": "<|endoftext|>",
|
| 6 |
+
"extra_special_tokens": [
|
| 7 |
+
"<|endoftext|>",
|
| 8 |
+
"[MASK]",
|
| 9 |
+
"[gMASK]",
|
| 10 |
+
"[sMASK]",
|
| 11 |
+
"<sop>",
|
| 12 |
+
"<eop>",
|
| 13 |
+
"<|system|>",
|
| 14 |
+
"<|user|>",
|
| 15 |
+
"<|assistant|>",
|
| 16 |
+
"<|observation|>",
|
| 17 |
+
"<|begin_of_image|>",
|
| 18 |
+
"<|end_of_image|>",
|
| 19 |
+
"<|begin_of_video|>",
|
| 20 |
+
"<|end_of_video|>",
|
| 21 |
+
"<|begin_of_audio|>",
|
| 22 |
+
"<|end_of_audio|>",
|
| 23 |
+
"<|begin_of_transcription|>",
|
| 24 |
+
"<|end_of_transcription|>"
|
| 25 |
+
],
|
| 26 |
+
"is_local": true,
|
| 27 |
+
"model_max_length": 1048576,
|
| 28 |
+
"model_specific_special_tokens": {},
|
| 29 |
+
"pad_token": "<|endoftext|>",
|
| 30 |
+
"padding_side": "left",
|
| 31 |
+
"remove_space": false,
|
| 32 |
+
"tokenizer_class": "TokenizersBackend"
|
| 33 |
+
}
|