Instructions to use clzoro/Qwen3.5-9B-Claude-distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use clzoro/Qwen3.5-9B-Claude-distill with Transformers:

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

pipe = pipeline("text-generation", model="clzoro/Qwen3.5-9B-Claude-distill")
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("clzoro/Qwen3.5-9B-Claude-distill")
model = AutoModelForMultimodalLM.from_pretrained("clzoro/Qwen3.5-9B-Claude-distill", 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 clzoro/Qwen3.5-9B-Claude-distill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "clzoro/Qwen3.5-9B-Claude-distill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clzoro/Qwen3.5-9B-Claude-distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/clzoro/Qwen3.5-9B-Claude-distill

SGLang

How to use clzoro/Qwen3.5-9B-Claude-distill 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 "clzoro/Qwen3.5-9B-Claude-distill" \
    --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": "clzoro/Qwen3.5-9B-Claude-distill",
		"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 "clzoro/Qwen3.5-9B-Claude-distill" \
        --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": "clzoro/Qwen3.5-9B-Claude-distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use clzoro/Qwen3.5-9B-Claude-distill with Docker Model Runner:
```
docker model run hf.co/clzoro/Qwen3.5-9B-Claude-distill
```

Qwen3.5-9B-Claude-distill / README.md

Kassadin

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	---
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE
	pipeline_tag: text-generation
	base_model: Qwen/Qwen3.5-9B
	tags:
	- qwen3.5
	- claude-distill
	language:
	- en
	- zh
	---

	# Qwen3.5-9B Claude-Distill

	A fine-tuned version of [Qwen/Qwen3.5-9B](Qwen/Qwen3.5-9B_URL) through knowledge distillation from Claude. This model is trained with full parameter fine-tuning on curated Claude reasoning traces.

	## Model Highlights

	- Claude-Distilled Reasoning: Trained on high-quality chain-of-thought reasoning traces distilled from Claude Opus
	- Multi-Domain Coverage: Math, logic, coding, creative writing, STEM, and multi-turn reasoning
	- Dense Architecture: Based on Qwen/Qwen3.5-9B with 9B parameters
	- Multimodal Capable: Inherits vision-language capabilities from Qwen3.5

	## Model Description

	\| Property \| Value \|
	\|----------\|-------\|
	\| Base Model \| Qwen/Qwen3.5-9B \|
	\| Model Type \| Causal Language Model with Vision Encoder \|
	\| Parameters \| 9B \|
	\| Languages \| English, Chinese \|
	\| License \| Apache 2.0 \|
	\| Developer \| [Kassadin88](https://huggingface.co/Kassadin88) \|

	## Training Data

	Distilled from Claude on the following datasets:

	\| Dataset \| Samples \| Description \|
	\|---------\|---------\|-------------\|
	\| [Claude Opus 4.5 High Reasoning](https://huggingface.co/datasets/dalisoft/claude-4.5-opus-high-reasoning-250x) \| 250 \| High reasoning depth samples \|
	\| [Claude Opus 4.6 Reasoning](https://huggingface.co/datasets/V3N0M/Jenna-Opus-4.6) \| 9,633 \| Math, logic puzzles, multi-step instructions with CoT \|
	\| [Claude Opus 4.6 High Reasoning](https://huggingface.co/datasets/dalisoft/claude-opus-4.6-high-reasoning-700x) \| 757 \| Coding and creative writing with adaptive reasoning \|
	\| [Claude Opus 4.6 Extended Reasoning](https://huggingface.co/datasets/Vezora/Claude-Opus-4.6-Reasoning-500x) \| 500 \| Extended reasoning across STEM and practical domains \|
	\| [Claude Opus 4.6 Extended Reasoning 887x](https://huggingface.co/datasets/Vezora/Claude-Opus-4.6-Reasoning-887x) \| 887 \| Tool calling, bullshit detection, multi-turn traces \|
	\| [Claude Sonnet & Opus 4.6 Reasoning](https://huggingface.co/datasets/riddlemeasured/Claude-Sonnet-X-Opus-4.6-Reasoning-small-500) \| 524 \| Natural human-written prompts from Reddit & Stack Overflow \|
	\| [Opus 4.6 Reasoning Filtered](https://huggingface.co/datasets/nickexyi/Opus-4.6-Reasoning-3000x-filtered) \| 2,326 \| Filtered reasoning traces (refusals removed) \|

	Total: ~14.9K samples

	### Data Composition

	\| Domain \| Percentage \| Description \|
	\|--------\|------------\|-------------\|
	\| Math & Logic \| ~40% \| Multi-step problem solving with chain-of-thought \|
	\| Coding \| ~25% \| Code generation, debugging, and algorithm design \|
	\| STEM \| ~15% \| Science, engineering, and extended reasoning \|
	\| Creative Writing \| ~10% \| Adaptive reasoning for creative tasks \|
	\| Multi-turn / Tool Use \| ~10% \| Tool calling, clarification, and dialogue \|

	## Benchmark Results

	![Benchmark Results](https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen3.5/Figures/qwen3.5_small_size_score.png)

	For detailed benchmark results and model architecture, please refer to the original [Qwen/Qwen3.5-9B](Qwen/Qwen3.5-9B_URL) model card.

	## Quickstart

	For full usage guide, please refer to the original [Qwen/Qwen3.5-9B](Qwen/Qwen3.5-9B_URL) model card.

	### Using with vLLM

	```bash
	vllm serve Kassadin88/Qwen3.5-9B-Claude-distill \
	--port 8000 \
	--tensor-parallel-size 2 \
	--max-model-len 32768 \
	--trust-remote-code \
	--reasoning-parser qwen3
	```

	### Using with SGLang

	```bash
	python -m sglang.launch_server \
	--model-path Kassadin88/Qwen3.5-9B-Claude-distill \
	--port 8000 \
	--tp-size 2 \
	--mem-fraction-static 0.8 \
	--context-length 32768 \
	--reasoning-parser qwen3
	```

	### Using with Hugging Face Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Kassadin88/Qwen3.5-9B-Claude-distill"

	tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto",
	trust_remote_code=True
	)

	messages = [
	{"role": "user", "content": "Hello, how are you?"}
	]
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=512
	)
	generated_ids = [
	output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
	]

	response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
	print(response)
	```

	## Usage Tips

	### For Reasoning Tasks

	```python
	messages = [
	{"role": "user", "content": "Solve step by step: What is the sum of all prime numbers less than 100?"}
	]
	# Model will use chain-of-thought reasoning from Claude distillation
	```

	### For Coding Tasks

	```python
	messages = [
	{"role": "user", "content": "Implement a binary search tree with insert, delete, and find operations in Python."}
	]
	# Model benefits from Claude's coding reasoning traces
	```

	### Enabling / Disabling Thinking

	```python
	# Enable thinking mode (recommended for reasoning tasks)
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)

	# Disable thinking mode (for simple tasks, faster inference)
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
	```

	## Limitations

	- This model is distilled from Claude and may inherit biases from the training data
	- The distillation dataset is relatively small (~14.9K samples), which may limit generalization
	- Should not be used for medical, legal, or financial advice without verification
	- The model's reasoning capabilities are constrained by the quality and diversity of the distillation data

	## Citation

	```bibtex
	@misc{qwen3.5-9b-claude-distill,
	author = {Kassadin88},
	title = {Qwen3.5-9B Claude-Distill: A Claude-Distilled Fine-Tuned Model},
	year = {2026},
	publisher = {HuggingFace},
	url = {https://huggingface.co/Kassadin88/Qwen3.5-9B-Claude-distill}
	}
	```

	## Acknowledgments

	- Base Model: [Qwen Team](https://github.com/QwenLM/Qwen3) for Qwen3.5
	- Training Data: Various Claude Opus reasoning datasets on HuggingFace
	- Training Framework: DeepSpeed

	---

	Note: This model is intended for research and educational purposes. Please use responsibly.