Instructions to use l3utterfly/open-llama-3b-v2-layla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3utterfly/open-llama-3b-v2-layla with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="l3utterfly/open-llama-3b-v2-layla")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("l3utterfly/open-llama-3b-v2-layla") model = AutoModelForCausalLM.from_pretrained("l3utterfly/open-llama-3b-v2-layla", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use l3utterfly/open-llama-3b-v2-layla with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "l3utterfly/open-llama-3b-v2-layla" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "l3utterfly/open-llama-3b-v2-layla", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/l3utterfly/open-llama-3b-v2-layla
- SGLang
How to use l3utterfly/open-llama-3b-v2-layla 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 "l3utterfly/open-llama-3b-v2-layla" \ --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": "l3utterfly/open-llama-3b-v2-layla", "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 "l3utterfly/open-llama-3b-v2-layla" \ --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": "l3utterfly/open-llama-3b-v2-layla", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use l3utterfly/open-llama-3b-v2-layla with Docker Model Runner:
docker model run hf.co/l3utterfly/open-llama-3b-v2-layla
Download pytorch_model.bin from l3utterfly/open-llama-3b-v2-layla: direct link, hf CLI and curl.
- Browser
- Download file 6.85 GB
-
https://huggingface.co/l3utterfly/open-llama-3b-v2-layla/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3utterfly/open-llama-3b-v2-layla/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3utterfly/open-llama-3b-v2-layla/resolve/main/pytorch_model.bin
6.85 GB
- Xet hash:
- 7b346d59c57a1e1801ef4b4553b07545000274f0ecc204453fde41eddf5cfc9a
- Size of remote file:
- 6.85 GB
- SHA256:
- 2af977123b3c23972e9c35b22b5fbafc3518f5edb20b6f2aa34882e85159f2a8
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