---
license: apache-2.0
base_model: Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
language:
  - en
  - zh
tags:
  - qwen3.5
  - gptq
  - int4
  - quantized
  - reasoning
  - multimodal
  - vision
  - moe
library_name: transformers
pipeline_tag: image-text-to-text
---

# Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4

This is a **GPTQ INT4 quantized** version of [Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled](https://huggingface.co/Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled).

Please refer to the original model card for details on the model architecture, training data, and capabilities.

> **Note**: While the original fine-tuning focused on text-only reasoning tasks, this model inherits multimodal capabilities from the base Qwen3.5-35B-A3B. The vision encoder is preserved and functional for image understanding tasks.

## Model Architecture

This is a **Mixture-of-Experts (MoE)** model with:
- **Total Parameters**: 35B
- **Active Parameters**: ~3B per token
- **Experts**: 256 total, 8 active per token

## Quantization Details

- **Method**: GPTQ (4-bit INT4, W4A16)
- **Group Size**: 128
- **Calibration**: 1024 samples from C4 dataset (~2048 tokens average)
- **Vision Encoder**: Preserved (not quantized)
- **MTP Module**: Preserved (not quantized)

## Usage with vLLM

### Text-only
```python
from vllm import LLM, SamplingParams

llm = LLM(
    model="codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4",
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.9,
)

sampling_params = SamplingParams(temperature=0.7, max_tokens=2048)
prompt = "Explain the difference between TCP and UDP protocols."
outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)
```

### With Image (Multimodal)
```python
from vllm import LLM, SamplingParams

llm = LLM(
    model="codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4",
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.9,
)

sampling_params = SamplingParams(temperature=0.7, max_tokens=256)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
            {"type": "text", "text": "What is in this image?"}
        ]
    }
]
outputs = llm.chat(messages, sampling_params)
print(outputs[0].outputs[0].text)
```

## Hardware Requirements

| Precision | VRAM (Approx.) |
|-----------|----------------|
| INT4 GPTQ | ~22 GB |

## Acknowledgements

- Original model by [Jackrong](https://huggingface.co/Jackrong)
- Base model: [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B)
- Quantization performed using [GPTQModel](https://github.com/ModelCloud/GPTQModel)

## License

Apache 2.0 (inherited from original model)