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/scripts/get_chat_template.py from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 2.94 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/scripts/get_chat_template.py
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/scripts/get_chat_template.py
-
curl -L -o get_chat_template.py https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/scripts/get_chat_template.py
2.94 kB
| #!/usr/bin/env python | |
| ''' | |
| Fetches the Jinja chat template of a HuggingFace model. | |
| If a model has multiple chat templates, you can specify the variant name. | |
| Syntax: | |
| ./scripts/get_chat_template.py model_id [variant] | |
| Examples: | |
| ./scripts/get_chat_template.py CohereForAI/c4ai-command-r-plus tool_use | |
| ./scripts/get_chat_template.py microsoft/Phi-3.5-mini-instruct | |
| ''' | |
| import json | |
| import re | |
| import sys | |
| def get_chat_template(model_id, variant=None): | |
| try: | |
| # Use huggingface_hub library if available. | |
| # Allows access to gated models if the user has access and ran `huggingface-cli login`. | |
| from huggingface_hub import hf_hub_download | |
| with open(hf_hub_download(repo_id=model_id, filename="tokenizer_config.json"), encoding="utf-8") as f: | |
| config_str = f.read() | |
| except ImportError: | |
| import requests | |
| assert re.match(r"^[\w.-]+/[\w.-]+$", model_id), f"Invalid model ID: {model_id}" | |
| response = requests.get(f"https://huggingface.co/{model_id}/resolve/main/tokenizer_config.json") | |
| if response.status_code == 401: | |
| raise Exception('Access to this model is gated, please request access, authenticate with `huggingface-cli login` and make sure to run `pip install huggingface_hub`') | |
| response.raise_for_status() | |
| config_str = response.text | |
| try: | |
| config = json.loads(config_str) | |
| except json.JSONDecodeError: | |
| # Fix https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json | |
| # (Remove extra '}' near the end of the file) | |
| config = json.loads(re.sub(r'\}([\n\s]*\}[\n\s]*\],[\n\s]*"clean_up_tokenization_spaces")', r'\1', config_str)) | |
| chat_template = config['chat_template'] | |
| if isinstance(chat_template, str): | |
| return chat_template | |
| else: | |
| variants = { | |
| ct['name']: ct['template'] | |
| for ct in chat_template | |
| } | |
| def format_variants(): | |
| return ', '.join(f'"{v}"' for v in variants.keys()) | |
| if variant is None: | |
| if 'default' not in variants: | |
| raise Exception(f'Please specify a chat template variant (one of {format_variants()})') | |
| variant = 'default' | |
| sys.stderr.write(f'Note: picked "default" chat template variant (out of {format_variants()})\n') | |
| elif variant not in variants: | |
| raise Exception(f"Variant {variant} not found in chat template (found {format_variants()})") | |
| return variants[variant] | |
| def main(args): | |
| if len(args) < 1: | |
| raise ValueError("Please provide a model ID and an optional variant name") | |
| model_id = args[0] | |
| variant = None if len(args) < 2 else args[1] | |
| template = get_chat_template(model_id, variant) | |
| sys.stdout.write(template) | |
| if __name__ == '__main__': | |
| main(sys.argv[1:]) | |