Instructions to use AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER: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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AJKADZ/PHI_CODER: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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
Use Docker
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AJKADZ/PHI_CODER with Ollama:
ollama run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AJKADZ/PHI_CODER with Docker Model Runner:
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Lemonade
How to use AJKADZ/PHI_CODER with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AJKADZ/PHI_CODER:Q4_K_M
Run and chat with the model
lemonade run user.PHI_CODER-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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Download phi-coder-hf/llama.cpp/examples/jeopardy/README.md from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/jeopardy/README.md
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/examples/jeopardy/README.md
-
curl -L -o README.md https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/jeopardy/README.md
1.05 kB
| # llama.cpp/example/jeopardy | |
| This is pretty much just a straight port of aigoopy/llm-jeopardy/ with an added graph viewer. | |
| The jeopardy test can be used to compare the fact knowledge of different models and compare them to each other. This is in contrast to some other tests, which test logical deduction, creativity, writing skills, etc. | |
| Step 1: Open jeopardy.sh and modify the following: | |
| ``` | |
| MODEL=(path to your model) | |
| MODEL_NAME=(name of your model) | |
| prefix=(basically, if you use vicuna it's Human: , if you use something else it might be User: , etc) | |
| opts=(add -instruct here if needed for your model, or anything else you want to test out) | |
| ``` | |
| Step 2: Run `jeopardy.sh` from the llama.cpp folder | |
| Step 3: Repeat steps 1 and 2 until you have all the results you need. | |
| Step 4: Run `graph.py`, and follow the instructions. At the end, it will generate your final graph. | |
| Note: The Human bar is based off of the full, original 100 sample questions. If you modify the question count or questions, it will not be valid. | |