Instructions to use OpenLemur/lemur-70b-chat-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenLemur/lemur-70b-chat-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenLemur/lemur-70b-chat-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpenLemur/lemur-70b-chat-v1") model = AutoModelForCausalLM.from_pretrained("OpenLemur/lemur-70b-chat-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenLemur/lemur-70b-chat-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenLemur/lemur-70b-chat-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenLemur/lemur-70b-chat-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenLemur/lemur-70b-chat-v1
- SGLang
How to use OpenLemur/lemur-70b-chat-v1 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 "OpenLemur/lemur-70b-chat-v1" \ --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": "OpenLemur/lemur-70b-chat-v1", "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 "OpenLemur/lemur-70b-chat-v1" \ --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": "OpenLemur/lemur-70b-chat-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenLemur/lemur-70b-chat-v1 with Docker Model Runner:
docker model run hf.co/OpenLemur/lemur-70b-chat-v1
Download pytorch_model-00012-of-00015.bin from OpenLemur/lemur-70b-chat-v1: direct link, hf CLI and curl.
- Browser
- Download file 9.8 GB
-
https://huggingface.co/OpenLemur/lemur-70b-chat-v1/resolve/refs%2Fpr%2F1/pytorch_model-00012-of-00015.bin
- Command line
-
hf download hf://OpenLemur/lemur-70b-chat-v1@refs/pr/1/pytorch_model-00012-of-00015.bin
-
curl -L -o pytorch_model-00012-of-00015.bin https://huggingface.co/OpenLemur/lemur-70b-chat-v1/resolve/refs%2Fpr%2F1/pytorch_model-00012-of-00015.bin
9.8 GB
- Xet hash:
- be92162f2d50eac7309d2653ddb667ea8e4c0407c6c53a6560171b9df679a211
- Size of remote file:
- 9.8 GB
- SHA256:
- 444bb1fd8052bea6dd781589d27a1b689649d2116a7f0415739e92f75b4f6087
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