Instructions to use calcuis/phi3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use calcuis/phi3 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf calcuis/phi3:Q4_K_M # Run inference directly in the terminal: llama cli -hf calcuis/phi3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf calcuis/phi3:Q4_K_M # Run inference directly in the terminal: llama cli -hf calcuis/phi3:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf calcuis/phi3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf calcuis/phi3:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf calcuis/phi3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf calcuis/phi3:Q4_K_M
Use Docker
docker model run hf.co/calcuis/phi3:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use calcuis/phi3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "calcuis/phi3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "calcuis/phi3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/calcuis/phi3:Q4_K_M
- Ollama
How to use calcuis/phi3 with Ollama:
ollama run hf.co/calcuis/phi3:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use calcuis/phi3 with Docker Model Runner:
docker model run hf.co/calcuis/phi3:Q4_K_M
- Lemonade
How to use calcuis/phi3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull calcuis/phi3:Q4_K_M
Run and chat with the model
lemonade run user.phi3-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 926 Bytes
2b0945e 522cdd9 2b0945e 017391b 40309c6 c1455ff 40309c6 5319afa 28198cc 5319afa 28198cc 5319afa 28198cc 5319afa 28198cc 5319afa 28198cc 5319afa 28198cc 16ca49a 49670bb 8e44694 49670bb d80b8ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ---
license: mit
language:
- en
base_model:
- microsoft/Phi-3-mini-128k-instruct
pipeline_tag: text-generation
---
## GGUF quantized version of Phi-3 Model (128k-instruct mini)
project original [source](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) (base model)
Q_2 (not nice)
Q_3 (acceptable)
Q_4 family is recommanded (good for running with CPU as well)
Q_5 (good in general)
Q_6 is good also; if you want a better result; take this one instead of Q_5
Q_8 which is very good; need a reasonable size of RAM otherwise you might expect a long wait
16-bit and 32-bit are also provided here for research perspectives; since the file size (16bit) is similar to the original safetensors; once you have a GPU, go ahead with the safetensors, pretty much the same
### how to run it
use any connector for interacting with gguf; i.e., [gguf-connector](https://pypi.org/project/gguf-connector/)
welcome to ai era |