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
Inquiries about customized trainer in open script
Hi, I read your source code in github, if I understand correct from the customized trainer.py, the expand layer is only modify specify layer. While reading your paper, you are adding layers on top instead fine tune specified layer. So want to clarify if i misunderstood your approach. Appreciate for your insights. Interesting works!
Thanks,
Ming
Thanks for your interest. We add the identity blocks interleaved to the initial model and then we tune the added block while freezing the other part. I hope this will be helpful.
I see, thank you so much! if we would like to refer to your github codes , i think we just need add the arguments expand_layers, once we specify it, the identity blocks will be auto created and tune accordingly?
Thanks,
Ming
I think you should first use https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py to create the ckpt with added blocks. After that, you can load the ckpt and specify the added blocks, which are going to be tuned, in the training script.