Instructions to use explosion-testing/llama2-kv-sharing-float16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use explosion-testing/llama2-kv-sharing-float16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="explosion-testing/llama2-kv-sharing-float16")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("explosion-testing/llama2-kv-sharing-float16") model = AutoModelForCausalLM.from_pretrained("explosion-testing/llama2-kv-sharing-float16") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use explosion-testing/llama2-kv-sharing-float16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "explosion-testing/llama2-kv-sharing-float16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "explosion-testing/llama2-kv-sharing-float16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/explosion-testing/llama2-kv-sharing-float16
- SGLang
How to use explosion-testing/llama2-kv-sharing-float16 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 "explosion-testing/llama2-kv-sharing-float16" \ --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": "explosion-testing/llama2-kv-sharing-float16", "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 "explosion-testing/llama2-kv-sharing-float16" \ --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": "explosion-testing/llama2-kv-sharing-float16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use explosion-testing/llama2-kv-sharing-float16 with Docker Model Runner:
docker model run hf.co/explosion-testing/llama2-kv-sharing-float16
- Xet hash:
- 49c32fc1d2812ee273b72abad1b2c62f3374aa89a0bbafdf45c7ffbf42aa0e0a
- Size of remote file:
- 6.63 MB
- SHA256:
- 3b0a2dd6970157ebd6d3eef57be42b1721175e3da4ebad826597f64423b73cb4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.