Text Generation
Transformers
Safetensors
hy_v3
compressed-tensors
LLM Compressor
vLLM
conversational
8-bit precision
Instructions to use RedHatAI/Hy3-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| 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 | |
| ``` | |