Instructions to use RedHatAI/Qwen3.6-35B-A3B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3.6-35B-A3B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="RedHatAI/Qwen3.6-35B-A3B-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("RedHatAI/Qwen3.6-35B-A3B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/Qwen3.6-35B-A3B-NVFP4", 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 RedHatAI/Qwen3.6-35B-A3B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3.6-35B-A3B-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": "RedHatAI/Qwen3.6-35B-A3B-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/RedHatAI/Qwen3.6-35B-A3B-NVFP4

SGLang

How to use RedHatAI/Qwen3.6-35B-A3B-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 "RedHatAI/Qwen3.6-35B-A3B-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": "RedHatAI/Qwen3.6-35B-A3B-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 "RedHatAI/Qwen3.6-35B-A3B-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": "RedHatAI/Qwen3.6-35B-A3B-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 RedHatAI/Qwen3.6-35B-A3B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3.6-35B-A3B-NVFP4
```

ChibuUkachi commited on Jul 10

Commit

c11656c

verified ·

1 Parent(s): e7759a5

add BFCL results

Browse files

Files changed (1) hide show

README.md +41 -1

README.md CHANGED Viewed

@@ -154,7 +154,10 @@ This model was evaluated on GSM8K-Platinum, MMLU-Pro, IFEval, Math 500, GPQA Dia
 | Math 500 | 84.80 | 85.00 | 100.24 |
 | Lcb Codegeneration V6 | 77.33 | 74.67 | 96.55 |
 | MMLU Pro Chat | 85.32 | 84.70 | 99.28 |
 ### Reproduction
@@ -246,5 +249,42 @@ lighteval endpoint litellm litellm_config.yaml \
   --save-details
 ```
 </details>

 | Math 500 | 84.80 | 85.00 | 100.24 |
 | Lcb Codegeneration V6 | 77.33 | 74.67 | 96.55 |
 | MMLU Pro Chat | 85.32 | 84.70 | 99.28 |
+| BFCLv4 Overall | 57.83 | 56.10 | 97.01% |
+| BFCLv4 Single Turn | 53.81 | 53.45 | 99.34% |
+| BFCLv4 Multi-Turn |62.25 | 58.13 |93.38%  |
+| BFCLv4 Agentic |49.91  | 49.31 | 98.80% |
 ### Reproduction
   --save-details
 ```
+#### BFCLv4
+BFCL requires the model to be registered in the leaderboard codebase before running evaluation.
+**Step 1 — Register the model in `bfcl_eval/constants/model_config.py`**
+Add the following entry to `api_inference_model_map`:
+```python
+"Qwen3.6-35B-A3B-NVFP4": ModelConfig(
+    model_name="Qwen3.6-35B-A3B-NVFP4",
+    display_name="Qwen3.6-35B-A3B-NVFP4 (FC)",
+    url="https://huggingface.co/RedHatAI/Qwen3.6-35B-A3B-NVFP4",
+    org="Google",
+    license="Apache 2.0",
+    model_handler=OpenAICompletionsHandler,
+    input_price=None,
+    output_price=None,
+    is_fc_model=True,
+    underscore_to_dot=True,
+),
+```
+**Step 2 — Add the key to `bfcl_eval/constants/supported_models.py`**
+Add `"Qwen3.6-35B-A3B-NVFP4"` to the `SUPPORTED_MODELS` list.
+**Step 3 — Start the vLLM server** (use the command at the top of this section; the `--served-model-name` flag ensures BFCL can find the model by its registered slug).
+**Step 4 — Generate responses and evaluate**
+```
+bfcl generate --model Qwen3.6-35B-A3B-NVFP4 --test-category all
+bfcl evaluate --model Qwen3.6-35B-A3B-NVFP4 --test-category all
+```
 </details>