Instructions to use cortexso/phi-3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/phi-3.5 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/phi-3.5", filename="phi-3.5-mini-instruct-q2_k.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cortexso/phi-3.5 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/phi-3.5:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/phi-3.5:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/phi-3.5:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/phi-3.5: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 cortexso/phi-3.5:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/phi-3.5: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 cortexso/phi-3.5:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/phi-3.5:Q4_K_M
Use Docker
docker model run hf.co/cortexso/phi-3.5:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/phi-3.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/phi-3.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/phi-3.5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/phi-3.5:Q4_K_M
- Ollama
How to use cortexso/phi-3.5 with Ollama:
ollama run hf.co/cortexso/phi-3.5:Q4_K_M
- Unsloth Studio new
How to use cortexso/phi-3.5 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/phi-3.5 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cortexso/phi-3.5 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/phi-3.5 to start chatting
- Docker Model Runner
How to use cortexso/phi-3.5 with Docker Model Runner:
docker model run hf.co/cortexso/phi-3.5:Q4_K_M
- Lemonade
How to use cortexso/phi-3.5 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/phi-3.5:Q4_K_M
Run and chat with the model
lemonade run user.phi-3.5-Q4_K_M
List all available models
lemonade list
license: mit
pipeline_tag: text-generation
tags:
- cortex.cpp
Overview
Microsoft developed and released the Phi-3.5 model, a state-of-the-art large language model built upon the Phi-3 architecture. With its focus on high-quality, reasoning-dense data, this model represents a significant advancement in instruction-tuned language models. Phi-3.5 has been fine-tuned through supervised learning, proximal policy optimization (PPO), and direct preference optimization (DPO) to ensure precise instruction following and robust safety measures. Supporting a 128K token context length, the model demonstrates exceptional performance in tasks requiring extended context understanding and complex reasoning. The model's training data consists of synthetic datasets and carefully filtered publicly available web content, inheriting the high-quality foundation established in the Phi-3 series.
Variants
| No | Variant | Cortex CLI command |
|---|---|---|
| 1 | Phi-3.5-3b | cortex run phi-3.5:3b |
Use it with Jan (UI)
- Install Jan using Quickstart
- Use in Jan model Hub:
cortexso/phi-3.5
Use it with Cortex (CLI)
- Install Cortex using Quickstart
- Run the model with command:
cortex run phi-3.5
Credits
- Author: Microsoft
- Converter: Homebrew
- Original License: License
- Papers: Phi-3.5 Paper