Instructions to use HawkonLi/Hunyuan-A52B-Instruct-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HawkonLi/Hunyuan-A52B-Instruct-2bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HawkonLi/Hunyuan-A52B-Instruct-2bit", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("HawkonLi/Hunyuan-A52B-Instruct-2bit", trust_remote_code=True, dtype="auto") - MLX
How to use HawkonLi/Hunyuan-A52B-Instruct-2bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("HawkonLi/Hunyuan-A52B-Instruct-2bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- vLLM
How to use HawkonLi/Hunyuan-A52B-Instruct-2bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HawkonLi/Hunyuan-A52B-Instruct-2bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HawkonLi/Hunyuan-A52B-Instruct-2bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HawkonLi/Hunyuan-A52B-Instruct-2bit
- SGLang
How to use HawkonLi/Hunyuan-A52B-Instruct-2bit 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 "HawkonLi/Hunyuan-A52B-Instruct-2bit" \ --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": "HawkonLi/Hunyuan-A52B-Instruct-2bit", "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 "HawkonLi/Hunyuan-A52B-Instruct-2bit" \ --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": "HawkonLi/Hunyuan-A52B-Instruct-2bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - MLX LM
How to use HawkonLi/Hunyuan-A52B-Instruct-2bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "HawkonLi/Hunyuan-A52B-Instruct-2bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "HawkonLi/Hunyuan-A52B-Instruct-2bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HawkonLi/Hunyuan-A52B-Instruct-2bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use HawkonLi/Hunyuan-A52B-Instruct-2bit with Docker Model Runner:
docker model run hf.co/HawkonLi/Hunyuan-A52B-Instruct-2bit
HawkonLi/Hunyuan-A52B-Instruct-2bit
Introduction
This Model was converted to MLX format from tencent-community/Hunyuan-A52B-Instruct
mlx-lm version: 0.21.0
convert-parameter:
q_group_size: 128
q_bits: 2
Based on testing, this model can BARELY run local inference on a MacBook Pro 16-inch (M3 Max, 128GB RAM) . The following command must be executed before running the model:
sudo sysctl iogpu.wired_limit_mb=105000
This command requires macOS 15.0 or higher to work.
This model requires 104,259 MB of memory, which is close to the maximum recommended size of 98,384 MB on the M3 Max with 128GB RAM, but it does fit. Therefore, the command above is used to increase the system's wired memory limit. Please note, this may cause unexpected system lag or interruptions.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("HawkonLi/Hunyuan-A52B-Instruct-2bit", tokenizer_config={"eos_token": "<|endoftext|>", "trust_remote_code": True},lazy=True)
prompt = "蓝牙耳机坏了,该去看牙科还是耳科"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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2-bit
Model tree for HawkonLi/Hunyuan-A52B-Instruct-2bit
Base model
tencent-community/Hunyuan-A52B-Instruct