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
Download phi-coder-hf/llama.cpp/examples/jeopardy/graph.py from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/jeopardy/graph.py
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/examples/jeopardy/graph.py
-
curl -L -o graph.py https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/jeopardy/graph.py
1.7 kB
| #!/usr/bin/env python3 | |
| import matplotlib.pyplot as plt | |
| import os | |
| import csv | |
| labels = [] | |
| numbers = [] | |
| numEntries = 1 | |
| rows = [] | |
| def bar_chart(numbers, labels, pos): | |
| plt.bar(pos, numbers, color='blue') | |
| plt.xticks(ticks=pos, labels=labels) | |
| plt.title("Jeopardy Results by Model") | |
| plt.xlabel("Model") | |
| plt.ylabel("Questions Correct") | |
| plt.show() | |
| def calculatecorrect(): | |
| directory = os.fsencode("./examples/jeopardy/results/") | |
| csv_reader = csv.reader(open("./examples/jeopardy/qasheet.csv", 'rt'), delimiter=',') | |
| for row in csv_reader: | |
| global rows | |
| rows.append(row) | |
| for listing in os.listdir(directory): | |
| filename = os.fsdecode(listing) | |
| if filename.endswith(".txt"): | |
| file = open("./examples/jeopardy/results/" + filename, "rt") | |
| global labels | |
| global numEntries | |
| global numbers | |
| labels.append(filename[:-4]) | |
| numEntries += 1 | |
| i = 1 | |
| totalcorrect = 0 | |
| for line in file.readlines(): | |
| if line.strip() != "------": | |
| print(line) | |
| else: | |
| print("Correct answer: " + rows[i][2] + "\n") | |
| i += 1 | |
| print("Did the AI get the question right? (y/n)") | |
| if input() == "y": | |
| totalcorrect += 1 | |
| numbers.append(totalcorrect) | |
| if __name__ == '__main__': | |
| calculatecorrect() | |
| pos = list(range(numEntries)) | |
| labels.append("Human") | |
| numbers.append(48.11) | |
| bar_chart(numbers, labels, pos) | |
| print(labels) | |
| print(numbers) | |