Instructions to use kodelow/Hy3-NVFP4-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kodelow/Hy3-NVFP4-W4A16 with Transformers:

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

pipe = pipeline("text-generation", model="kodelow/Hy3-NVFP4-W4A16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kodelow/Hy3-NVFP4-W4A16")
model = AutoModelForCausalLM.from_pretrained("kodelow/Hy3-NVFP4-W4A16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kodelow/Hy3-NVFP4-W4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kodelow/Hy3-NVFP4-W4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kodelow/Hy3-NVFP4-W4A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kodelow/Hy3-NVFP4-W4A16

SGLang

How to use kodelow/Hy3-NVFP4-W4A16 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 "kodelow/Hy3-NVFP4-W4A16" \
    --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": "kodelow/Hy3-NVFP4-W4A16",
		"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 "kodelow/Hy3-NVFP4-W4A16" \
        --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": "kodelow/Hy3-NVFP4-W4A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kodelow/Hy3-NVFP4-W4A16 with Docker Model Runner:
```
docker model run hf.co/kodelow/Hy3-NVFP4-W4A16
```

olka-fi commited on 6 days ago

Commit

f65d233

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@@ -20,6 +20,9 @@ tags:
 > **A 4-bit NVFP4 quantization of [`tencent/Hy3`](https://huggingface.co/tencent/Hy3).**
 > The original model card is preserved in full [below](#model-introduction).
 Weight-only NVFP4 quant produced with [**qstream**](https://github.com/olka/qstream)
 using MSE-optimal group-scale selection: the routed MoE experts (≈95% of the weights)
 are quantized to 4-bit; everything quality-sensitive stays BF16.

 > **A 4-bit NVFP4 quantization of [`tencent/Hy3`](https://huggingface.co/tencent/Hy3).**
 > The original model card is preserved in full [below](#model-introduction).
+2x GB10 full recipe, scripts, and all the bugs documented by Tony DeAngelo (tonyd2wild):
+https://github.com/tonyd2wild/Hy3-295B-NVFP4-MTP-2x-DGX-Spark
 Weight-only NVFP4 quant produced with [**qstream**](https://github.com/olka/qstream)
 using MSE-optimal group-scale selection: the routed MoE experts (≈95% of the weights)
 are quantized to 4-bit; everything quality-sensitive stays BF16.