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: 1,643 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 | import pytest
from utils import *
server = ServerPreset.tinyllama2()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
def test_tokenize_detokenize():
global server
server.start()
# tokenize
content = "What is the capital of France ?"
res_tok = server.make_request("POST", "/tokenize", data={
"content": content
})
assert res_tok.status_code == 200
assert len(res_tok.body["tokens"]) > 5
# detokenize
res_detok = server.make_request("POST", "/detokenize", data={
"tokens": res_tok.body["tokens"],
})
assert res_detok.status_code == 200
assert res_detok.body["content"].strip() == content
def test_tokenize_with_bos():
global server
server.start()
# tokenize
content = "What is the capital of France ?"
bosId = 1
res_tok = server.make_request("POST", "/tokenize", data={
"content": content,
"add_special": True,
})
assert res_tok.status_code == 200
assert res_tok.body["tokens"][0] == bosId
def test_tokenize_with_pieces():
global server
server.start()
# tokenize
content = "This is a test string with unicode 媽 and emoji 🤗"
res_tok = server.make_request("POST", "/tokenize", data={
"content": content,
"with_pieces": True,
})
assert res_tok.status_code == 200
for token in res_tok.body["tokens"]:
assert "id" in token
assert token["id"] > 0
assert "piece" in token
assert len(token["piece"]) > 0
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