Text Generation
Transformers
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use mervinpraison/phi-3-openassistant-guanaco-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mervinpraison/phi-3-openassistant-guanaco-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mervinpraison/phi-3-openassistant-guanaco-sft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mervinpraison/phi-3-openassistant-guanaco-sft", device_map="auto") - PEFT
How to use mervinpraison/phi-3-openassistant-guanaco-sft with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mervinpraison/phi-3-openassistant-guanaco-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mervinpraison/phi-3-openassistant-guanaco-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mervinpraison/phi-3-openassistant-guanaco-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mervinpraison/phi-3-openassistant-guanaco-sft
- SGLang
How to use mervinpraison/phi-3-openassistant-guanaco-sft 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 "mervinpraison/phi-3-openassistant-guanaco-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mervinpraison/phi-3-openassistant-guanaco-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "mervinpraison/phi-3-openassistant-guanaco-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mervinpraison/phi-3-openassistant-guanaco-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mervinpraison/phi-3-openassistant-guanaco-sft with Docker Model Runner:
docker model run hf.co/mervinpraison/phi-3-openassistant-guanaco-sft
Download training_args.bin from mervinpraison/phi-3-openassistant-guanaco-sft: direct link, hf CLI and curl.
- Browser
- Download file 6.26 kB
-
https://huggingface.co/mervinpraison/phi-3-openassistant-guanaco-sft/resolve/main/training_args.bin
- Command line
-
hf download hf://mervinpraison/phi-3-openassistant-guanaco-sft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mervinpraison/phi-3-openassistant-guanaco-sft/resolve/main/training_args.bin
6.26 kB
- Xet hash:
- 09f511f93ea410c2efb36539ca7f63b9fa236517ad6d51396218a309539e7371
- Size of remote file:
- 6.26 kB
- SHA256:
- 3a420e1f57ac48ade287bc789c8f61ba0cd70eb4436209e8432fa3ffa67fb6c4
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