Instructions to use CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound")
model = AutoModelForImageTextToText.from_pretrained("CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound")
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 CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound

SGLang

How to use CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound 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 "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound" \
    --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": "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound",
		"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 "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound" \
        --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": "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound with Docker Model Runner:
```
docker model run hf.co/CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound
```

Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound

Int4 AutoRound quantized version of TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2.

Base model: TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2
Quantization: INT4 symmetric, group_size=128 (W4A16)
Algorithm: AutoRound
Format: AutoRound (compatible with vLLM, SGLang, compressed-tensors)

Usage

Serve with vLLM

vllm serve CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound \
    --tensor-parallel-size 1 \
    --max-model-len 262144 \
    --gpu-memory-utilization 0.95 \
    --reasoning-parser qwen3

With speculative decoding (Qwen3 MTP)

vllm serve CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound \
    --tensor-parallel-size 1 \
    --max-model-len 262144 \
    --gpu-memory-utilization 0.95 \
    --reasoning-parser qwen3 \
    --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

Load in Python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound"
model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

Quantization Details

Parameter	Value
Bits	4
Group size	128
Symmetric	Yes
Calibration	NeelNanda/pile-10k
Seq length	2048

License

Please follow the license of the original model (TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2).

Downloads last month: 1,049

Safetensors

Model size

6B params

Tensor type

I32

BF16

F16

Model tree for CoreWorxLab/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2-int4-AutoRound

Base model

Qwen/Qwen3.6-27B

Finetuned

TeichAI/Qwen3.6-27B-Claude-Opus-Reasoning-Distill-v2

Quantized

(10)

this model