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
File size: 2,665 Bytes
9b19fd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import pytest
from utils import *
server = ServerPreset.tinyllama_infill()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama_infill()
def test_infill_without_input_extra():
global server
server.start()
res = server.make_request("POST", "/infill", data={
"input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
"prompt": " int n_threads = llama_",
"input_suffix": "}\n",
})
assert res.status_code == 200
assert match_regex("(Ann|small|shiny|Daddy)+", res.body["content"])
def test_infill_with_input_extra():
global server
server.start()
res = server.make_request("POST", "/infill", data={
"input_extra": [{
"filename": "llama.h",
"text": "LLAMA_API int32_t llama_n_threads();\n"
}],
"input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
"prompt": " int n_threads = llama_",
"input_suffix": "}\n",
})
assert res.status_code == 200
assert match_regex("(Dad|excited|park)+", res.body["content"])
@pytest.mark.parametrize("input_extra", [
{},
{"filename": "ok"},
{"filename": 123},
{"filename": 123, "text": "abc"},
{"filename": 123, "text": 456},
])
def test_invalid_input_extra_req(input_extra):
global server
server.start()
res = server.make_request("POST", "/infill", data={
"input_extra": [input_extra],
"input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
"prompt": " int n_threads = llama_",
"input_suffix": "}\n",
})
assert res.status_code == 400
assert "error" in res.body
@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
def test_with_qwen_model():
global server
server.model_file = None
server.model_hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-IQ3_XXS-GGUF"
server.model_hf_file = "qwen2.5-coder-1.5b-iq3_xxs-imat.gguf"
server.start(timeout_seconds=600)
res = server.make_request("POST", "/infill", data={
"input_extra": [{
"filename": "llama.h",
"text": "LLAMA_API int32_t llama_n_threads();\n"
}],
"input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
"prompt": " int n_threads = llama_",
"input_suffix": "}\n",
})
assert res.status_code == 200
assert res.body["content"] == "n_threads();\n printf(\"Number of threads: %d\\n\", n_threads);\n return 0;\n"
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