Text Generation
Safetensors
vllm
hy_v3
gptq
4-bit precision
quantization
hunyuan
hy3
Mixture of Experts
rtn
conversational
Instructions to use avtc/Hy3-GPTQ-RTN-4bit-tp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use avtc/Hy3-GPTQ-RTN-4bit-tp8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "avtc/Hy3-GPTQ-RTN-4bit-tp8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "avtc/Hy3-GPTQ-RTN-4bit-tp8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/avtc/Hy3-GPTQ-RTN-4bit-tp8
- SGLang
How to use avtc/Hy3-GPTQ-RTN-4bit-tp8 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 "avtc/Hy3-GPTQ-RTN-4bit-tp8" \ --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": "avtc/Hy3-GPTQ-RTN-4bit-tp8", "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 "avtc/Hy3-GPTQ-RTN-4bit-tp8" \ --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": "avtc/Hy3-GPTQ-RTN-4bit-tp8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use avtc/Hy3-GPTQ-RTN-4bit-tp8 with Docker Model Runner:
docker model run hf.co/avtc/Hy3-GPTQ-RTN-4bit-tp8
fits perfect on 4 x A6000
#1
by WiSFoR - opened
With turbo quant enabled, barely makes to 262144 max context length.
MTP support could fit too (0.96 gpu) but resulted to gibberish output when enabled.
This could be the best model to run on ampere gpu since deepseek support on it has no promise.
Thank you for this model 😃