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: 2,386 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 | import pytest
from utils import *
import base64
import requests
server: ServerProcess
IMG_URL_0 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/11_truck.png"
IMG_URL_1 = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/91_cat.png"
response = requests.get(IMG_URL_0)
response.raise_for_status() # Raise an exception for bad status codes
IMG_BASE64_0 = "data:image/png;base64," + base64.b64encode(response.content).decode("utf-8")
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.tinygemma3()
@pytest.mark.parametrize(
"prompt, image_url, success, re_content",
[
# test model is trained on CIFAR-10, but it's quite dumb due to small size
("What is this:\n", IMG_URL_0, True, "(cat)+"),
("What is this:\n", "IMG_BASE64_0", True, "(cat)+"), # exceptional, so that we don't cog up the log
("What is this:\n", IMG_URL_1, True, "(frog)+"),
("Test test\n", IMG_URL_1, True, "(frog)+"), # test invalidate cache
("What is this:\n", "malformed", False, None),
("What is this:\n", "https://google.com/404", False, None), # non-existent image
("What is this:\n", "https://ggml.ai", False, None), # non-image data
# TODO @ngxson : test with multiple images, no images and with audio
]
)
def test_vision_chat_completion(prompt, image_url, success, re_content):
global server
server.start(timeout_seconds=60) # vision model may take longer to load due to download size
if image_url == "IMG_BASE64_0":
image_url = IMG_BASE64_0
res = server.make_request("POST", "/chat/completions", data={
"temperature": 0.0,
"top_k": 1,
"messages": [
{"role": "user", "content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {
"url": image_url,
}},
]},
],
})
if success:
assert res.status_code == 200
choice = res.body["choices"][0]
assert "assistant" == choice["message"]["role"]
assert match_regex(re_content, choice["message"]["content"])
else:
assert res.status_code != 200
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