Instructions to use RedHatAI/Hy3-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Hy3-NVFP4-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Hy3-NVFP4-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Hy3-NVFP4-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Hy3-NVFP4-FP8")
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 RedHatAI/Hy3-NVFP4-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Hy3-NVFP4-FP8"
# 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/Hy3-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Hy3-NVFP4-FP8

SGLang

How to use RedHatAI/Hy3-NVFP4-FP8 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/Hy3-NVFP4-FP8" \
    --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/Hy3-NVFP4-FP8",
		"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 "RedHatAI/Hy3-NVFP4-FP8" \
        --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/Hy3-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Hy3-NVFP4-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Hy3-NVFP4-FP8
```

Hy3-NVFP4-FP8

File size: 862 Bytes

39b2a3e

---
license: mit
base_model:
- tencent/Hy3
library_name: transformers
tags:
- compressed-tensors
- LLM Compressor
- vLLM
---

# RedHatAI/Hy3-NVFP4-FP8

This is a quantized version of `tencent/Hy3` with MoE layers quantized to NVFP4 and attention layers quantized to FP8 block

## Usage

This model is intended for deployment with vLLM. You can serve the model using

```bash
vllm serve RedHatAI/Hy3-NVFP4-FP8 \
    --tensor-parallel-size 4 \
    --tool-call-parser hy_v3 \
    --enable-auto-tool-choice \
    --reasoning-parser hy_v3 \
    --port 8089
```

## Creation Process
https://github.com/vllm-project/llm-compressor/pull/2928

## Evaluation ##

In progress, currently approximately fully recovery

```bash
inspect eval hf/Idavidrein/gpqa/diamond   --model vllm/RedHatAI/Hy3-NVFP4-FP8 --reasoning-effort high --model-base-url http://localhost:8089/v1
```