Image-Text-to-Text
Transformers
Safetensors
qwen3_5_moe
vision
Mixture of Experts
conversational
Not-For-All-Audiences
abliterated
heretic
uncensor
hermes-lineage
mtp
coding
tool-calling
reasoning
roleplay
bf16
Instructions to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT") 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)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT") model = AutoModelForMultimodalLM.from_pretrained("oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", 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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", "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/oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT
- SGLang
How to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT 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 "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT" \ --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": "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", "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 "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT" \ --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": "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", "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 oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with Docker Model Runner:
docker model run hf.co/oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT
Document completed Q4 llama.cpp diagnostic, including failures and unscored tasks
Browse files- BENCHMARK-DIAGNOSTIC.json +212 -0
- README.md +16 -0
- SHA256SUMS +2 -1
BENCHMARK-DIAGNOSTIC.json
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| 1 |
+
{
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| 2 |
+
"generated_utc": "2026-09-04T23:47:51.958393+00:00",
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"profile": {
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"model": "Q36-v1.3-Q4_K_M.gguf",
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"backend": "llama.cpp",
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| 14 |
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"per_prompt_seconds": 45,
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| 15 |
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"note": "User-requested llama.cpp Q4 evaluation; not directly comparable to the earlier thinking-enabled BF16 run. Selected adapters still require official evaluators.",
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| 16 |
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"gpu_inventory": "NVIDIA H200, 570.124.06, 143771 MiB",
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| 17 |
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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| 197 |
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}
|
| 198 |
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},
|
| 199 |
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"median_tps_including_prefill_for_outputs_ge64_tokens": 156.35807976215648,
|
| 200 |
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"speed_sample_count": 104,
|
| 201 |
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"limitations": [
|
| 202 |
+
"Compact mixed diagnostic, not official benchmark leaderboard results.",
|
| 203 |
+
"No meaningful overall accuracy is claimed across heterogeneous tasks and incomplete evaluators.",
|
| 204 |
+
"Strict scores combine required answer content and output format; semantically correct but misformatted outputs can fail.",
|
| 205 |
+
"Coding execution tests, several mathematics tasks and subjective rubrics still require their evaluators/review.",
|
| 206 |
+
"Greedy decoding, thinking disabled. Repetition/token-limit failures remain visible; the Transformers helper is not active in llama.cpp.",
|
| 207 |
+
"Speed includes prefill and is workload dependent, not a pure decode microbenchmark.",
|
| 208 |
+
"Only Q4_K_M on H200 was benchmarked here. This does not establish BF16/other-quant or other-device quality/performance.",
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| 209 |
+
"No completed original-Huihui comparison; no claim of outperforming the source model.",
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| 210 |
+
"204 available prompts from the larger proposed matrix; unavailable adapters are documented in the private preparation report."
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| 211 |
+
]
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| 212 |
+
}
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README.md
CHANGED
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@@ -91,3 +91,19 @@ The helper is opt-in: plain Transformers, GGUF and Ollama imports do not automat
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| 91 |
execute it. These safeguards do not change weights and do not fix factual errors.
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| 92 |
Selected regression tests are documented in `RUNTIME-PATCH-VALIDATION.json`; they
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are not independent benchmark scores or evidence of universal improvement.
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execute it. These safeguards do not change weights and do not fix factual errors.
|
| 92 |
Selected regression tests are documented in `RUNTIME-PATCH-VALIDATION.json`; they
|
| 93 |
are not independent benchmark scores or evidence of universal improvement.
|
| 94 |
+
|
| 95 |
+
## Compact Q4_K_M diagnostic (H200)
|
| 96 |
+
|
| 97 |
+
The llama.cpp **Q4_K_M** diagnostic attempted 204 available prompts:
|
| 98 |
+
186 completed, 18 were incomplete or errored.
|
| 99 |
+
Of 108 completed outputs with implemented strict automatic checks,
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| 100 |
+
61 passed. Another 78 completed outputs require
|
| 101 |
+
manual review or an official evaluator. These heterogeneous counts are **not a composite
|
| 102 |
+
model accuracy score** and are not official leaderboard benchmarks.
|
| 103 |
+
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| 104 |
+
The run used greedy decoding with thinking disabled, a 16K context, a 4,096-token
|
| 105 |
+
output cap and per-request time limits. Repetition failures still occurred; inference
|
| 106 |
+
limits do not fix learned reasoning errors. Only Q4_K_M on H200 was tested in this
|
| 107 |
+
run, with no completed source-model comparison. See `BENCHMARK-DIAGNOSTIC.json`
|
| 108 |
+
for per-task counts, configuration, timings and limitations. Raw test prompts,
|
| 109 |
+
answers and internal logs are kept outside the public model repository.
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SHA256SUMS
CHANGED
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@@ -1,5 +1,6 @@
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| 1 |
20a2a90fa761fe5081d31d25989d94656ad7b9766103b4244b71891e714dcc22 LICENSE
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| 2 |
-
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b36b05ac4d31eb371f841af813f3b3fdfad5c04375c58214013a94dd929c2ad0 RELEASE-VALIDATION.json
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| 4 |
3836329a27f1f7c4320c8aef72fd617f8ab4440e88e7419ed5cf785408c48cb2 RUNTIME-PATCH-VALIDATION.json
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55d4931433fe502b794226ee7f4d206a6bdd436ac9f80eb7d8ebb4c639f9ea0c chat_template.jinja
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+
807d54b88902798393fc104c3ca80d3e424d64270e8d1e1a34a44d4574f52c7e BENCHMARK-DIAGNOSTIC.json
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20a2a90fa761fe5081d31d25989d94656ad7b9766103b4244b71891e714dcc22 LICENSE
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| 3 |
+
57f8ca092a5579c5b41fb94dc540a7eafdefb746128fa30752f82272e10716e9 README.md
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| 4 |
b36b05ac4d31eb371f841af813f3b3fdfad5c04375c58214013a94dd929c2ad0 RELEASE-VALIDATION.json
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| 5 |
3836329a27f1f7c4320c8aef72fd617f8ab4440e88e7419ed5cf785408c48cb2 RUNTIME-PATCH-VALIDATION.json
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| 6 |
55d4931433fe502b794226ee7f4d206a6bdd436ac9f80eb7d8ebb4c639f9ea0c chat_template.jinja
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