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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,122 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3.8-27B
+tags:
+  - int4
+  - w4a16
+  - awq
+  - gptq
+  - llm-compressor
+  - compressed-tensors
+  - vllm
+library_name: transformers
+---
+
+# Qwen3.8-27B-INT4-AWQ-GPTQ (GDN-4bit)
+
+Mixed-precision **INT4 (W4A16)** quantization of [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B),
+built with [llm-compressor](https://github.com/vllm-project/llm-compressor) using
+**AWQ activation-aware scaling followed by GPTQ**.
+
+**21 GB, and measurably more faithful to the BF16 model than the official FP8 release**
+on high-confidence token agreement — at 8 GB less. Runs on anything Turing or newer
+(no FP8/FP4 hardware required).
+
+This is the size-optimised variant. A 23 GB sibling,
+[`Qwen3.8-27B-INT4-AWQ-GPTQ`](https://huggingface.co/TelperionAI/Qwen3.8-27B-INT4-AWQ-GPTQ),
+keeps the GDN projections at 8-bit and is more accurate again (0.93% vs 1.15% confident).
+
+## Recipe
+
+| component | precision |
+|---|---|
+| `mlp.{gate,up,down}_proj`, layers 0–55 | **INT4, group-32, asymmetric** (~4.6 effective bits) |
+| `mlp.{gate,up,down}_proj`, layers 56–63 | INT8 W8A16 |
+| `self_attn.{q,k,v,o}_proj` | INT8 W8A16 |
+| `linear_attn.{in_proj_qkv,in_proj_z,out_proj}` (GDN) | **INT4, group-32, asymmetric** |
+| `lm_head`, `embed_tokens`, norms, GDN state params, vision tower | BF16 |
+
+Two passes:
+
+1. **AWQ** — per-input-channel scaling on `post_attention_layernorm → {gate_proj, up_proj}`
+   and `up_proj → down_proj`. Gate and up share one input, so the reciprocal scale folds
+   into the norm weights: zero size and zero throughput cost.
+2. **GPTQ** — Hessian-based error compensation, `actorder="static"`, `dampening_frac=0.01`.
+
+Calibration: 924 sequences × 1024 tokens of a balanced Nemotron-v2 blend
+(25% code, 25% math, 20% STEM, 20% chat, 10% multilingual).
+
+`lm_head` and `embed_tokens` stay BF16, matching Qwen's own official FP8 release.
+
+## Benchmarks
+
+Against the BF16 base on 142,727 tokens of self-distilled thinking-mode output plus
+200 free greedy generations. vLLM 0.27.1, TP=2, 2×B300.
+
+| checkpoint | size ↓ | top-1 ↑ | near-tie ↓ | moderate ↓ | confident ↓ | certain ↓ | divmed ↑ | tok/s ↑ |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| `Qwen/Qwen3.8-27B-FP8` *(8-bit ref)* | 29 GB | 96.15% | 22.70% | 3.48% | 1.45% | 0.08% | 47 | 8711 |
+| **this model (GDN 4-bit)** | 21 GB | **95.88%** | **25.26%** | **3.60%** | **1.15%** | 0.13% | **51** | 4716 |
+| our 23 GB sibling (GDN 8-bit) | 23 GB | 96.30% | 22.29% | 3.52% | 0.93% | 0.09% | 48 | 4617 |
+| `cyankiwi/Qwen3.8-27B-AWQ-INT4` | **20 GB** | 94.35% | 33.74% | 5.59% | 1.35% | **0.12%** | 29 | 4787 |
+
+Bold marks the best value in each column **among the ~20–21 GB checkpoints**; the FP8 row and
+our own 23 GB sibling sit in different size classes and are shown for reference only.
+
+**Columns.** `top-1` is raw argmax agreement with BF16. The four bucket columns are
+*disagreement* rates, split by how confident the base model was at that position
+(top1−top2 logprob margin): `near-tie` <0.5, `moderate` 0.5–2, `confident` 2–5,
+`certain` >5. **Only `confident` and `certain` are real damage** — a flip where the base
+model was itself nearly tied is numerical noise. `divmed` is the median token index at
+which free greedy generation first diverges from BF16 (higher is better).
+
+**Perplexity is deliberately excluded.** On this model it is anti-correlated with quality —
+the FP4 checkpoint with the best perplexity we measured also had the worst `certain`-bucket
+damage. Do not rank quantizations of this model by perplexity.
+
+### What the GDN precision costs
+
+Moving the GDN projections from 8-bit to 4-bit is what takes this build from 23 GB to 21 GB.
+It costs `confident` **0.93% → 1.15%** and `certain` **0.09% → 0.13%** — real but modest.
+Note the two are not a pure bit-width comparison: at 4-bit the GDN projections also move
+into GPTQ's error compensation, where at 8-bit they were plain round-to-nearest, so the
+true cost of 4-bit GDN in isolation is likely a little larger than shown.
+
+At matched size against `cyankiwi` (20 GB), this build is still ahead on `confident`
+(1.15% vs 1.35%) and level on `certain` — so the AWQ+GPTQ recipe, not the extra bits,
+accounts for most of the margin.
+
+### Why INT4 rather than NVFP4
+
+At ~4.6 effective bits, INT4 group-32 **asymmetric** gives 16 uniformly-spaced levels plus
+a per-group zero point. NVFP4 gives 8 non-uniform FP4 levels at 4.5 bits with no zero point.
+On this model the zero point matters — MLP weight distributions are skewed — and the same
+recipe in NVFP4 lands at 1.85% confident versus 0.93% here.
+
+NVFP4's advantage is **hardware**, not numerics: on Blackwell it decodes at 10590 tok/s
+against 4617 here, because native NVFP4 tensor-core paths beat Marlin INT4. Pick NVFP4 if
+you are throughput-bound on Blackwell; pick this if you want fidelity, or you are on
+Ampere/Ada where FP4 and FP8 do not exist.
+
+## Usage
+
+```python
+from vllm import LLM
+llm = LLM("TelperionAI/Qwen3.8-27B-INT4-AWQ-GPTQ-gdn4", tensor_parallel_size=2)
+```
+
+W4A16 needs only compute capability ≥ 7.5 (Turing), so this runs on 3090/4090/A100/H100
+as well as Blackwell. Throughput above was measured on B300 and is not representative of
+Ampere, where Marlin INT4 is comparatively better tuned.
+
+## Limitations
+
+- **21 GB is still tight on a single 24 GB card** once KV cache is accounted for.
+  Quantizing `lm_head` would free a further ~1.3 GB but was not measured here.
+- **Throughput on Blackwell is ~2.3× below NVFP4.** This checkpoint trades speed for fidelity.
+- **The MTP head is not included** — `Qwen3_5ForConditionalGeneration` does not carry it in
+  its state dict, so it is dropped during quantization. MTP speculative decoding is unavailable.
+- **Single evaluation corpus.** All numbers come from one self-distilled corpus. The margins
+  over FP8 and cyankiwi are statistically solid but have not been replicated on a second
+  distribution, nor on downstream task benchmarks.
+- Vision tower untouched (BF16); evaluated as a text model.

chat_template.jinja

@@ -0,0 +1,170 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- set reasoning_instructions = '' %}
+{%- if enable_thinking is undefined or enable_thinking is true %}
+    {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}
+    {%- if resolved_reasoning_effort not in ('xhigh', 'medium', 'low') %}
+        {{- raise_exception('Unexpected reasoning effort ' ~ reasoning_effort ~ '. Supported types are xhigh (default), medium, and low.') }}
+    {%- endif %}
+    {%- if resolved_reasoning_effort == 'xhigh' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}
+    {%- elif resolved_reasoning_effort == 'low' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}
+    {%- endif %}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {%- if reasoning_instructions %}
+        {{- reasoning_instructions + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '<|im_start|>system\n' + (reasoning_instructions + '\n\n' if reasoning_instructions else '')  + content + '<|im_end|>\n' }}
+        {%- elif reasoning_instructions %}
+            {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+        {%- endif %}
+    {%- elif reasoning_instructions %}
+        {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if preserve_thinking is undefined or preserve_thinking is true or loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined and tool_call.arguments != '' %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,505 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 248056,
+  "language_model_only": false,
+  "model_type": "qwen3_5",
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "re:.*layers\\.([0-9]|[1-4][0-9]|5[0-5])\\.mlp\\.(gate|up|down)_proj$",
+          "re:.*linear_attn\\.(in_proj_qkv|in_proj_z|out_proj)$"
+        ],
+        "weights": {
+          "actorder": "static",
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 32,
+          "num_bits": 4,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "group",
+          "symmetric": false,
+          "type": "int",
+          "zp_dtype": "torch.int8"
+        }
+      },
+      "group_1": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "re:.*self_attn\\.(q|k|v|o)_proj$",
+          "re:.*layers\\.(5[6-9]|6[0-3])\\.mlp\\.(gate|up|down)_proj$"
+        ],
+        "weights": {
+          "actorder": "static",
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 128,
+          "num_bits": 8,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "model.visual.blocks.0.attn.qkv",
+      "model.visual.blocks.0.attn.proj",
+      "model.visual.blocks.0.mlp.linear_fc1",
+      "model.visual.blocks.0.mlp.linear_fc2",
+      "model.visual.blocks.1.attn.qkv",
+      "model.visual.blocks.1.attn.proj",
+      "model.visual.blocks.1.mlp.linear_fc1",
+      "model.visual.blocks.1.mlp.linear_fc2",
+      "model.visual.blocks.2.attn.qkv",
+      "model.visual.blocks.2.attn.proj",
+      "model.visual.blocks.2.mlp.linear_fc1",
+      "model.visual.blocks.2.mlp.linear_fc2",
+      "model.visual.blocks.3.attn.qkv",
+      "model.visual.blocks.3.attn.proj",
+      "model.visual.blocks.3.mlp.linear_fc1",
+      "model.visual.blocks.3.mlp.linear_fc2",
+      "model.visual.blocks.4.attn.qkv",
+      "model.visual.blocks.4.attn.proj",
+      "model.visual.blocks.4.mlp.linear_fc1",
+      "model.visual.blocks.4.mlp.linear_fc2",
+      "model.visual.blocks.5.attn.qkv",
+      "model.visual.blocks.5.attn.proj",
+      "model.visual.blocks.5.mlp.linear_fc1",
+      "model.visual.blocks.5.mlp.linear_fc2",
+      "model.visual.blocks.6.attn.qkv",
+      "model.visual.blocks.6.attn.proj",
+      "model.visual.blocks.6.mlp.linear_fc1",
+      "model.visual.blocks.6.mlp.linear_fc2",
+      "model.visual.blocks.7.attn.qkv",
+      "model.visual.blocks.7.attn.proj",
+      "model.visual.blocks.7.mlp.linear_fc1",
+      "model.visual.blocks.7.mlp.linear_fc2",
+      "model.visual.blocks.8.attn.qkv",
+      "model.visual.blocks.8.attn.proj",
+      "model.visual.blocks.8.mlp.linear_fc1",
+      "model.visual.blocks.8.mlp.linear_fc2",
+      "model.visual.blocks.9.attn.qkv",
+      "model.visual.blocks.9.attn.proj",
+      "model.visual.blocks.9.mlp.linear_fc1",
+      "model.visual.blocks.9.mlp.linear_fc2",
+      "model.visual.blocks.10.attn.qkv",
+      "model.visual.blocks.10.attn.proj",
+      "model.visual.blocks.10.mlp.linear_fc1",
+      "model.visual.blocks.10.mlp.linear_fc2",
+      "model.visual.blocks.11.attn.qkv",
+      "model.visual.blocks.11.attn.proj",
+      "model.visual.blocks.11.mlp.linear_fc1",
+      "model.visual.blocks.11.mlp.linear_fc2",
+      "model.visual.blocks.12.attn.qkv",
+      "model.visual.blocks.12.attn.proj",
+      "model.visual.blocks.12.mlp.linear_fc1",
+      "model.visual.blocks.12.mlp.linear_fc2",
+      "model.visual.blocks.13.attn.qkv",
+      "model.visual.blocks.13.attn.proj",
+      "model.visual.blocks.13.mlp.linear_fc1",
+      "model.visual.blocks.13.mlp.linear_fc2",
+      "model.visual.blocks.14.attn.qkv",
+      "model.visual.blocks.14.attn.proj",
+      "model.visual.blocks.14.mlp.linear_fc1",
+      "model.visual.blocks.14.mlp.linear_fc2",
+      "model.visual.blocks.15.attn.qkv",
+      "model.visual.blocks.15.attn.proj",
+      "model.visual.blocks.15.mlp.linear_fc1",
+      "model.visual.blocks.15.mlp.linear_fc2",
+      "model.visual.blocks.16.attn.qkv",
+      "model.visual.blocks.16.attn.proj",
+      "model.visual.blocks.16.mlp.linear_fc1",
+      "model.visual.blocks.16.mlp.linear_fc2",
+      "model.visual.blocks.17.attn.qkv",
+      "model.visual.blocks.17.attn.proj",
+      "model.visual.blocks.17.mlp.linear_fc1",
+      "model.visual.blocks.17.mlp.linear_fc2",
+      "model.visual.blocks.18.attn.qkv",
+      "model.visual.blocks.18.attn.proj",
+      "model.visual.blocks.18.mlp.linear_fc1",
+      "model.visual.blocks.18.mlp.linear_fc2",
+      "model.visual.blocks.19.attn.qkv",
+      "model.visual.blocks.19.attn.proj",
+      "model.visual.blocks.19.mlp.linear_fc1",
+      "model.visual.blocks.19.mlp.linear_fc2",
+      "model.visual.blocks.20.attn.qkv",
+      "model.visual.blocks.20.attn.proj",
+      "model.visual.blocks.20.mlp.linear_fc1",
+      "model.visual.blocks.20.mlp.linear_fc2",
+      "model.visual.blocks.21.attn.qkv",
+      "model.visual.blocks.21.attn.proj",
+      "model.visual.blocks.21.mlp.linear_fc1",
+      "model.visual.blocks.21.mlp.linear_fc2",
+      "model.visual.blocks.22.attn.qkv",
+      "model.visual.blocks.22.attn.proj",
+      "model.visual.blocks.22.mlp.linear_fc1",
+      "model.visual.blocks.22.mlp.linear_fc2",
+      "model.visual.blocks.23.attn.qkv",
+      "model.visual.blocks.23.attn.proj",
+      "model.visual.blocks.23.mlp.linear_fc1",
+      "model.visual.blocks.23.mlp.linear_fc2",
+      "model.visual.blocks.24.attn.qkv",
+      "model.visual.blocks.24.attn.proj",
+      "model.visual.blocks.24.mlp.linear_fc1",
+      "model.visual.blocks.24.mlp.linear_fc2",
+      "model.visual.blocks.25.attn.qkv",
+      "model.visual.blocks.25.attn.proj",
+      "model.visual.blocks.25.mlp.linear_fc1",
+      "model.visual.blocks.25.mlp.linear_fc2",
+      "model.visual.blocks.26.attn.qkv",
+      "model.visual.blocks.26.attn.proj",
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recipe.yaml

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+default_stage:
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+    AWQModifier:
+      requires_calibration_data: true
+      mappings:
+      - smooth_layer: re:.*post_attention_layernorm$
+        balance_layers: ['re:.*mlp\.gate_proj$', 're:.*mlp\.up_proj$']
+        activation_hook_target: null
+      - smooth_layer: re:.*mlp\.up_proj$
+        balance_layers: ['re:.*mlp\.down_proj$']
+        activation_hook_target: null
+      offload_device: !!python/object/apply:torch.device [cpu]
+      duo_scaling: true
+      n_grid: 20
+    GPTQModifier:
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+        g0:
+          targets: ['re:.*layers\.([0-9]|[1-4][0-9]|5[0-5])\.mlp\.(gate|up|down)_proj$', 're:.*linear_attn\.(in_proj_qkv|in_proj_z|out_proj)$']
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+            observer_kwargs: {}
+          input_activations: null
+          output_activations: null
+          format: null
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+            observer_kwargs: {}
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+      dampening_frac: 0.01
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+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

video_preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 25165824,
+        "shortest_edge": 4096
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "video_processor_type": "Qwen3VLVideoProcessor"
+}