Instructions to use maghrane/Llama-2-7b-chat-finetune_test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maghrane/Llama-2-7b-chat-finetune_test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maghrane/Llama-2-7b-chat-finetune_test2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maghrane/Llama-2-7b-chat-finetune_test2") model = AutoModelForCausalLM.from_pretrained("maghrane/Llama-2-7b-chat-finetune_test2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use maghrane/Llama-2-7b-chat-finetune_test2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maghrane/Llama-2-7b-chat-finetune_test2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maghrane/Llama-2-7b-chat-finetune_test2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/maghrane/Llama-2-7b-chat-finetune_test2
- SGLang
How to use maghrane/Llama-2-7b-chat-finetune_test2 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 "maghrane/Llama-2-7b-chat-finetune_test2" \ --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": "maghrane/Llama-2-7b-chat-finetune_test2", "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 "maghrane/Llama-2-7b-chat-finetune_test2" \ --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": "maghrane/Llama-2-7b-chat-finetune_test2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use maghrane/Llama-2-7b-chat-finetune_test2 with Docker Model Runner:
docker model run hf.co/maghrane/Llama-2-7b-chat-finetune_test2
- Xet hash:
- b8aa70eba311d097c03b2cad7312fcdbe04d8547b95e537f97564f9652ae78fc
- Size of remote file:
- 9.98 GB
- SHA256:
- 706fda698ef644c0f9aef02fff8cee2c912ae9a87538c342a74174b3029d9e87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.