Instructions to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

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 Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
./llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

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 Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Use Docker

docker model run hf.co/Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

LM Studio
Jan
Ollama
How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Ollama:
```
ollama run hf.co/Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
```

Unsloth Studio new

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit to start chatting

Docker Model Runner
How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Docker Model Runner:
```
docker model run hf.co/Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16
```

Lemonade

How to use Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit:F16

Run and chat with the model

lemonade run user.Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit-F16

List all available models

lemonade list

🐍 Phi 3.5 Mini Instruct Python Coding Assistant Gguf 16Bit

Python code generation specialist. 171+ downloads. Fully local.

🎯 Python-First Design

Fine-tuned exclusively for Python code generation with:

50,000+ Python scripts from GitHub
200,000 Stack Overflow Q&A pairs
15,000 Jupyter notebooks
PEP 8 compliant output
Type hints and docstrings

🚀 Quick Start

Via Ollama

ollama run Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit

Via Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit",
    torch_dtype="auto", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Agnuxo/Phi-3.5-mini-instruct-python_coding_assistant-GGUF_16bit")

prompt = "Write a Python function to parse JSON and validate schema"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔗 P2PCLAW Ecosystem

Component	Purpose	Link
CAJAL-9B	Scientific papers	HF Model
CAJAL-4B	Lightweight papers	HF Model
BenchClaw	Code evaluation	HF Space
P2PCLAW	Research network	Website

👤 Author

Francisco Angulo de Lafuente (Agnuxo1) · ORCID: 0009-0001-1634-7063

Built with 🔥 by the P2PCLAW Collective

Downloads last month: 171

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

16-bit

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