Instructions to use mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic")
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("mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic")
model = AutoModelForMultimodalLM.from_pretrained("mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic", 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 mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic

SGLang

How to use mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic 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 "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic" \
    --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": "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic" \
        --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": "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic
```

mconcat commited on Mar 30

Commit

b95ff10

verified ·

1 Parent(s): 5299380

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Files changed (14) hide show

.gitattributes +1 -0
README.md +157 -0
chat_template.jinja +88 -0
config.json +474 -0
generation_config.json +7 -0
model.mtp.safetensors +3 -0
model.safetensors +3 -0
model.safetensors.index.json +0 -0
preprocessor_config.json +27 -0
processor_config.json +63 -0
recipe.yaml +39 -0
tokenizer.json +3 -0
tokenizer_config.json +33 -0
video_preprocessor_config.json +33 -0

.gitattributes CHANGED Viewed

@@ -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

 *.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 ADDED Viewed

	@@ -0,0 +1,157 @@

+---
+language:
+- en
+- zh
+license: apache-2.0
+base_model: Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2
+tags:
+- qwen3.5
+- reasoning
+- quantized
+- fp8
+- fp8-dynamic
+- compressed-tensors
+- deltanet
+- chain-of-thought
+- mtp
+pipeline_tag: text-generation
+library_name: transformers
+model_name: Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic
+quantized_by: mconcat
+---
+# Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic
+FP8_DYNAMIC quantized version of [Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2).
+This checkpoint keeps the same hybrid Qwen3.5 DeltaNet + softmax architecture and Qwen3.5 MTP head as the BF16 source, but quantizes most linear layers to FP8 W8A8 while leaving the most sensitive projections and sidecar components in BF16.
+The published folder includes:
+- `model.safetensors`
+- `model.safetensors.index.json`
+- `model.mtp.safetensors`
+- `processor_config.json`
+- `preprocessor_config.json`
+- `video_preprocessor_config.json`
+- `recipe.yaml`
+## Verified Inference
+Local export and sanity-check evaluation were verified on **2026-03-31** on a single **NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96 GB)** with:
+- `transformers==5.3.0`
+- `llm-compressor==0.10.1.dev40+g5ae2e149`
+- `vllm==0.17.1`
+What was verified in that run:
+- the FP8 export completed successfully
+- `model.mtp.safetensors` was restored into the output folder
+- the checkpoint loads in `transformers` with `device_map="auto"`
+- a quick perplexity sanity check against the BF16 source completed successfully
+vLLM is still the intended serving path for this family of models, but full local serve validation for this exact FP8 v2 export is still pending.
+## Quantization Strategy
+Uniform FP8_DYNAMIC quantization using [llm-compressor](https://github.com/vllm-project/llm-compressor):
+| Precision | Layers |
+|-----------|--------|
+| **FP8 W8A8** | most `Linear` layers |
+| **BF16** | `lm_head`, `embed_tokens`, `self_attn.o_proj`, DeltaNet `linear_attn.out_proj`, DeltaNet `in_proj_a`/`in_proj_b`, visual encoder, MTP sidecar |
+FP8 details:
+- weights: FP8, per-channel, static scales
+- input activations: FP8, per-token, dynamic scales
+- output activations: not explicitly quantized
+Architecture match with the BF16 source:
+- `model_type=qwen3_5`
+- `64` text layers
+- `full_attention_interval=4`
+- `mtp_num_hidden_layers=1`
+- `max_position_embeddings=262144`
+## Local Benchmark Slice
+Local quick sanity comparison against the BF16 v2 source model, run on **2026-03-31** with a small FineWeb-Edu perplexity slice.
+This is a reduced verification slice, not a full benchmark run.
+| Benchmark | BF16 v2 | FP8 v2 |
+|-----------|---------|--------|
+| FineWeb-Edu perplexity (20 samples, 13,531 tokens, max_len=1024) | **7.0758** | **7.1051** |
+Absolute perplexity delta:
+- `+0.0293`
+- about `+0.41%` relative to BF16
+## Usage
+### vLLM
+```bash
+pip install -U vllm==0.17.1 transformers==5.3.0
+```
+Expected serving command:
+```bash
+vllm serve mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic \
+  --max-model-len 262144 \
+  --gpu-memory-utilization 0.85 \
+  --kv-cache-dtype fp8_e4m3 \
+  --max-num-seqs 1 \
+  --skip-mm-profiling \
+  --reasoning-parser qwen3
+```
+With MTP enabled:
+```bash
+vllm serve mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic \
+  --max-model-len 262144 \
+  --gpu-memory-utilization 0.85 \
+  --kv-cache-dtype fp8_e4m3 \
+  --max-num-seqs 1 \
+  --skip-mm-profiling \
+  --reasoning-parser qwen3 \
+  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'
+```
+### Transformers
+```python
+from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration
+import torch
+model = Qwen3_5ForConditionalGeneration.from_pretrained(
+    "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-FP8-Dynamic",
+    trust_remote_code=True,
+)
+```
+## Compatibility
+| Framework | Supported | Notes |
+|-----------|-----------|-------|
+| vLLM >= 0.17.0 | Expected | Intended serving path; this exact v2 FP8 export still needs full local serve re-validation |
+| transformers >= 5.3.0 | Yes | Direct loading works with `device_map="auto"` |
+| SGLang | Unknown | Not verified for this export |
+## Notes
+- This export keeps `self_attn.o_proj` and DeltaNet `linear_attn.out_proj` in BF16 rather than FP8.
+- The output folder includes the Qwen3.5 MTP sidecar and processor metadata needed for serving compatibility.
+- The perplexity numbers above are intended as a quick sanity check, not as an exhaustive benchmark submission.

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,88 @@

+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|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" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|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 %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant
+<think>
+' }}
+{%- endif %}

config.json ADDED Viewed

	@@ -0,0 +1,474 @@

+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 248046,
+  "image_token_id": 248056,
+  "model_name": "qwen/Qwen3.5-27B",
+  "model_type": "qwen3_5",
+  "pad_token_id": 248044,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "float-quantized",
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": true,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": null,
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "token",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "channel",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "float-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",
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+      "model.visual.blocks.26.mlp.linear_fc1",
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+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "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+",
+  "processor_class": "Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

video_preprocessor_config.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "do_sample_frames": true,
+  "fps": 2,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "max_frames": 768,
+  "merge_size": 2,
+  "min_frames": 4,
+  "patch_size": 16,
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "return_metadata": false,
+  "size": {
+    "longest_edge": 25165824,
+    "shortest_edge": 4096
+  },
+  "temporal_patch_size": 2,
+  "video_processor_type": "Qwen3VLVideoProcessor"
+}