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
- Llama new
- 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: 3,458 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import pytest
from utils import *
server = ServerPreset.tinyllama2()
@pytest.fixture(scope="module", autouse=True)
def create_server():
global server
server = ServerPreset.tinyllama2()
server.slot_save_path = "./tmp"
server.temperature = 0.0
def test_slot_save_restore():
global server
server.start()
# First prompt in slot 1 should be fully processed
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of France?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Whiskers|Flana)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed
# Save state of slot 1
res = server.make_request("POST", "/slots/1?action=save", data={
"filename": "slot1.bin",
})
assert res.status_code == 200
assert res.body["n_saved"] == 84
# Since we have cache, this should only process the last tokens
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of Germany?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Jack|said)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 6 # only different part is processed
# Loading the saved cache into slot 0
res = server.make_request("POST", "/slots/0?action=restore", data={
"filename": "slot1.bin",
})
assert res.status_code == 200
assert res.body["n_restored"] == 84
# Since we have cache, slot 0 should only process the last tokens
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of Germany?",
"id_slot": 0,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Jack|said)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 6 # only different part is processed
# For verification that slot 1 was not corrupted during slot 0 load, same thing should work
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of Germany?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Jack|said)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 1
def test_slot_erase():
global server
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of France?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Whiskers|Flana)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed
# erase slot 1
res = server.make_request("POST", "/slots/1?action=erase")
assert res.status_code == 200
# re-run the same prompt, it should process all tokens again
res = server.make_request("POST", "/completion", data={
"prompt": "What is the capital of France?",
"id_slot": 1,
"cache_prompt": True,
})
assert res.status_code == 200
assert match_regex("(Whiskers|Flana)+", res.body["content"])
assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed
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