Instructions to use YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound", 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 YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-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": "YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound

SGLang

How to use YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-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 "YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-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": "YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-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 "YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-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": "YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound with Docker Model Runner:
```
docker model run hf.co/YuYu1015/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-int4-AutoRound
```

YuYu1015 commited on Apr 11

Commit

a70c48a

verified ·

1 Parent(s): fcf8f5a

Update README.md

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README.md +9 -6

README.md CHANGED Viewed

@@ -1,11 +1,15 @@
 ---
-license: other
-license_name: qwen
-license_link: https://huggingface.co/Qwen/Qwen3.5-35B-A3B/blob/main/LICENSE
-base_model: huihui-ai/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated
 tags:
   - qwen3.5
   - moe
   - int4
   - auto-round
   - gptq
@@ -13,8 +17,7 @@ tags:
   - abliterated
   - dgx-spark
   - dflash
-library_name: transformers
-pipeline_tag: text-generation
 language:
   - en
   - zh

 ---
+license: apache-2.0
+base_model:
+  - huihui-ai/Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated
+base_model_relation: quantized
+library_name: transformers
+pipeline_tag: text-generation
 tags:
+  - safetensors
   - qwen3.5
   - moe
+  - 4-bit
   - int4
   - auto-round
   - gptq
   - abliterated
   - dgx-spark
   - dflash
+  - vllm
 language:
   - en
   - zh