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/data_core/hemisphere_leftlayer_3.py from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder/data_core/hemisphere_leftlayer_3.py
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder/data_core/hemisphere_leftlayer_3.py
-
curl -L -o hemisphere_leftlayer_3.py https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder/data_core/hemisphere_leftlayer_3.py
1.38 kB
| from ..recursion_packet import RecursionPacket | |
| class LayerL3: | |
| """ | |
| ΞΛΩ – Left Hemisphere Layer 3 | |
| Purpose: Memory Signal Harmonization | |
| Receives recursion seeds, compares to symbolic traces in memory, | |
| and injects harmonic resonance tags into the packet. | |
| Aligns current recursion signals to past structures without mutating them. | |
| This layer stabilizes recursion with memory coherence. | |
| """ | |
| def __init__(self): | |
| self.known_structures = { | |
| "quantum_engine": ["Φ", "Ψ", "Θ"], | |
| "harmonic_clock": ["Θ", "ε", "Φ"], | |
| "translator": ["Ψ", "ε"], | |
| } | |
| def process(self, packet: RecursionPacket) -> RecursionPacket: | |
| seeds = packet.annotations.get("R2_seeds", []) | |
| memory_resonance = [] | |
| for seed in seeds: | |
| token = seed["origin_token"] | |
| glyph = seed["symbol"] | |
| for memory_id, structure in self.known_structures.items(): | |
| if glyph in structure: | |
| memory_resonance.append({ | |
| "seed": token, | |
| "glyph": glyph, | |
| "memory_tag": memory_id, | |
| "score": structure.count(glyph) | |
| }) | |
| packet.annotations["L3_memory_match"] = memory_resonance | |
| return packet | |