Instructions to use TencentARC/LLaMA-Pro-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TencentARC/LLaMA-Pro-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TencentARC/LLaMA-Pro-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TencentARC/LLaMA-Pro-8B") model = AutoModelForCausalLM.from_pretrained("TencentARC/LLaMA-Pro-8B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TencentARC/LLaMA-Pro-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TencentARC/LLaMA-Pro-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TencentARC/LLaMA-Pro-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TencentARC/LLaMA-Pro-8B
- SGLang
How to use TencentARC/LLaMA-Pro-8B 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 "TencentARC/LLaMA-Pro-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TencentARC/LLaMA-Pro-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "TencentARC/LLaMA-Pro-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TencentARC/LLaMA-Pro-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TencentARC/LLaMA-Pro-8B with Docker Model Runner:
docker model run hf.co/TencentARC/LLaMA-Pro-8B
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license: llama2
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# LLaMA-Pro-8B Model Card
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| CodeLLaMA-7B | 39.93 | 60.80 | 31.12 | 37.82 | 64.01 | 5.16 | 25.20 | 33.50 | 41.40 | 37.66 |
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| LLAMA PRO-INSTRUCT | 52.30 | 76.88 | 52.57 | 48.80 | 72.53 | 43.59 | 55.61 | 44.51 | 37.88 | 53.8 |
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### Performance on GPT4 Evaluation
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| Model | MT Bench |
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While LLaMA-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.
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## Ethical Considerations
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Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.
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license: llama2
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library_name: allennlp
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# LLaMA-Pro-8B Model Card
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| CodeLLaMA-7B | 39.93 | 60.80 | 31.12 | 37.82 | 64.01 | 5.16 | 25.20 | 33.50 | 41.40 | 37.66 |
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| LLAMA PRO-INSTRUCT | 52.30 | 76.88 | 52.57 | 48.80 | 72.53 | 43.59 | 55.61 | 44.51 | 37.88 | 53.8 |
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### Performance on GPT4 Evaluation # based on?
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| Model | MT Bench |
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While LLaMA-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.
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## Ethical Considerations
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Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.
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