Instructions to use tencent/Hunyuan-A13B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Hunyuan-A13B-Instruct-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tencent/Hunyuan-A13B-Instruct-FP8", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-A13B-Instruct-FP8", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tencent/Hunyuan-A13B-Instruct-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tencent/Hunyuan-A13B-Instruct-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": "tencent/Hunyuan-A13B-Instruct-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tencent/Hunyuan-A13B-Instruct-FP8
- SGLang
How to use tencent/Hunyuan-A13B-Instruct-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 "tencent/Hunyuan-A13B-Instruct-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": "tencent/Hunyuan-A13B-Instruct-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 "tencent/Hunyuan-A13B-Instruct-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": "tencent/Hunyuan-A13B-Instruct-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tencent/Hunyuan-A13B-Instruct-FP8 with Docker Model Runner:
docker model run hf.co/tencent/Hunyuan-A13B-Instruct-FP8
Upload tokenizer_config.json with huggingface_hub
Browse files- tokenizer_config.json +25 -0
tokenizer_config.json
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{
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"added_tokens_decoder": {},
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"additional_special_tokens": [
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"<|startoftext|>",
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"<|extra_0|>",
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"<|extra_4|>",
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"<|extra_5|>",
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"<|eos|>"
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],
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"architectures": [
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"GPT2LMHeadModel"
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],
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"auto_map": {
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"AutoTokenizer": [
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"tokenization_hy.HYTokenizer",
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null
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]
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},
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"chat_template": "{% set context = {'has_head': true} %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['content'] %}{% if loop.index0 == 0 %}{% if content == '' %}{% set _ = context.update({'has_head': false}) %}{% elif message['role'] == 'system' %}{% set content = '<|startoftext|>' + content + '<|extra_4|>' %}{% endif %}{% endif %}{% if message['role'] == 'user' %}{% if loop.index0 == 1 and not context.has_head %}{% set content = '<|startoftext|>' + content %}{% endif %}{% if loop.index0 == 1 and context.has_head %}{% set content = content + '<|extra_0|>' %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_0|>' %}{% endif %}{% elif message['role'] == 'assistant' %}{% set content = content + '<|eos|>' %}{% endif %}{{ content }}{% endfor %}",
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"clean_up_tokenization_spaces": true,
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"model_max_length": 1048576,
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"model_type": "gpt2",
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"pad_token": "<|pad|>",
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"tokenizer_class": "HYTokenizer"
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}
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