Instructions to use codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4")
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("codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4")
model = AutoModelForMultimodalLM.from_pretrained("codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4")
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 codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4",
		"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/codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4

SGLang

How to use codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4 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 "codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4" \
    --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": "codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4",
		"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 "codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4" \
        --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": "codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4",
		"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 codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4 with Docker Model Runner:
```
docker model run hf.co/codgician/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4
```

Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GPTQ-int4 / README.md

codgician

Update to improved quantization (1024 C4 samples, ~2048 tokens avg)

5709aea verified 3 months ago

preview code

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metadata

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.

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

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)

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
Base model: Qwen/Qwen3.5-35B-A3B
Quantization performed using GPTQModel

License

Apache 2.0 (inherited from original model)