Instructions to use rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4")
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("rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4")
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 rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4",
		"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/rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4

SGLang

How to use rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4 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 "rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4" \
    --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": "rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4",
		"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 "rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4" \
        --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": "rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4",
		"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 rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4 with Docker Model Runner:
```
docker model run hf.co/rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4
```

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4

This repository contains a ModelOpt-quantized checkpoint derived from Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled.

Quantization Summary

Quantization tool: NVIDIA TensorRT Model Optimizer
Weight quantization: NVFP4
KV cache quantization: FP8
Calibration size: 1024 samples
Calibration sequence length: 4096
Calibration batch size: 1
Calibration source mix:
- nohurry/Opus-4.6-Reasoning-3000x-filtered: 596 samples
- Jackrong/Qwen3.5-reasoning-700x: 178 samples
- TeichAI/claude-4.5-opus-high-reasoning-250x: 250 samples

The calibration set was converted into a single JSONL file with the source model's chat template applied before PTQ, so the activation distribution is closer to the reasoning format used by this distilled checkpoint.

Runtime Notes

Intended runtime target: SGLang with ModelOpt-compatible HF checkpoint loading
Quantization format: ModelOpt HF export, not compressed-tensors
A few unsupported or intentionally skipped modules may remain excluded by ModelOpt during export; see hf_quant_config.json for the final exclusion list

Files

model.safetensors: quantized weights
hf_quant_config.json: final quantization metadata
tokenizer and processor files inherited from the source checkpoint

Downloads last month: 5

Safetensors

Model size

17B params

Tensor type

BF16

F8_E4M3

Model tree for rhoninseiei/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-ModelOpt-NVFP4

Base model

Qwen/Qwen3.5-27B

Finetuned

Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

Quantized

(39)

this model