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/tools/server/tests/unit/test_ctx_shift.py from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 2.88 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/server/tests/unit/test_ctx_shift.py
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/tools/server/tests/unit/test_ctx_shift.py
-
curl -L -o test_ctx_shift.py https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/server/tests/unit/test_ctx_shift.py
2.88 kB
| import pytest | |
| from utils import * | |
| server = ServerPreset.tinyllama2() | |
| LONG_TEXT = """ | |
| Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. | |
| Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. | |
| Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. | |
| Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. | |
| """.strip() | |
| def create_server(): | |
| global server | |
| server = ServerPreset.tinyllama2() | |
| server.n_ctx = 256 | |
| server.n_slots = 2 | |
| def test_ctx_shift_enabled(): | |
| # the prompt is 301 tokens | |
| # the slot context is 256/2 = 128 tokens | |
| # the prompt is truncated to keep the last 109 tokens | |
| # 64 tokens are generated thanks to shifting the context when it gets full | |
| global server | |
| server.start() | |
| res = server.make_request("POST", "/completion", data={ | |
| "n_predict": 64, | |
| "prompt": LONG_TEXT, | |
| }) | |
| assert res.status_code == 200 | |
| assert res.body["timings"]["prompt_n"] == 109 | |
| assert res.body["timings"]["predicted_n"] == 64 | |
| assert res.body["truncated"] is True | |
| def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool): | |
| global server | |
| server.disable_ctx_shift = True | |
| server.n_predict = -1 | |
| server.start() | |
| res = server.make_request("POST", "/completion", data={ | |
| "n_predict": n_predict, | |
| "prompt": "Hi how are you", | |
| }) | |
| assert res.status_code == 200 | |
| assert res.body["timings"]["predicted_n"] == n_token_output | |
| assert res.body["truncated"] == truncated | |
| def test_ctx_shift_disabled_long_prompt(): | |
| global server | |
| server.disable_ctx_shift = True | |
| server.start() | |
| res = server.make_request("POST", "/completion", data={ | |
| "n_predict": 64, | |
| "prompt": LONG_TEXT, | |
| }) | |
| assert res.status_code != 200 | |
| assert "error" in res.body | |
| assert "exceeds the available context size" in res.body["error"]["message"] | |
| def test_ctx_shift_disabled_stream(): | |
| global server | |
| server.disable_ctx_shift = True | |
| server.start() | |
| res = server.make_stream_request("POST", "/v1/completions", data={ | |
| "n_predict": 256, | |
| "prompt": "Once", | |
| "stream": True, | |
| }) | |
| content = "" | |
| for data in res: | |
| choice = data["choices"][0] | |
| if choice["finish_reason"] == "length": | |
| assert len(content) > 0 | |
| else: | |
| assert choice["finish_reason"] is None | |
| content += choice["text"] | |