Instructions to use hpcai-tech/Colossal-LLaMA-2-7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hpcai-tech/Colossal-LLaMA-2-7b-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hpcai-tech/Colossal-LLaMA-2-7b-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base") model = AutoModelForCausalLM.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hpcai-tech/Colossal-LLaMA-2-7b-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hpcai-tech/Colossal-LLaMA-2-7b-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hpcai-tech/Colossal-LLaMA-2-7b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hpcai-tech/Colossal-LLaMA-2-7b-base
- SGLang
How to use hpcai-tech/Colossal-LLaMA-2-7b-base 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 "hpcai-tech/Colossal-LLaMA-2-7b-base" \ --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": "hpcai-tech/Colossal-LLaMA-2-7b-base", "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 "hpcai-tech/Colossal-LLaMA-2-7b-base" \ --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": "hpcai-tech/Colossal-LLaMA-2-7b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hpcai-tech/Colossal-LLaMA-2-7b-base with Docker Model Runner:
docker model run hf.co/hpcai-tech/Colossal-LLaMA-2-7b-base
Commit History
-
Adding Evaluation Results
This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card. If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions3a4777d
-
Update README.md
d7888b3
-
update community link
1f30e4f
-
Update README.md
73aa194 -
Update README.md
fbf819d -
Update README.md
0fed5a1
-
Update README.md
dbfcfb7 -
Add README.md (#1)
- Add README.md (8087a0ba3c28778b9b65d5b626f66cf5bf36fd5d)90c95f3
-
initial commit for testing
971880e -
initial commit
84bba08