gemma-4-e4b-python-lora

A LoRA adapter that fine-tunes mlx-community/gemma-4-e4b-4bit for Python code generation, trained with mlx_lm.

This repo contains only the adapter weights — you load it on top of the base model, you don't need to download a separate fused model.

Training details

Base model	mlx-community/gemma-4-e4b-4bit
Dataset	iamtarun/python_code_instructions_18k_alpaca
Fine-tune type	LoRA
LoRA rank	8
LoRA scale	20
LoRA dropout	0.0
Layers adapted	16
Learning rate	1e-5
Optimizer	Adam
Iterations	500

Training examples were formatted as:

### User:
{instruction}

### Assistant:
{output}

Usage

mlx_lm.load needs the adapter on disk, so download it locally first, then load it exactly as you would a local adapter:

pip install -U mlx-lm huggingface_hub

from huggingface_hub import snapshot_download
from mlx_lm import load, generate

adapter_path = snapshot_download("Prashanth-24/gemma-4-e4b-python-lora")
model, tokenizer = load("mlx-community/gemma-4-e4b-4bit", adapter_path=adapter_path)

prompt = "### User:\nwrite a python code to ADD two numbers\n\n### Assistant:\n"
print(generate(model, tokenizer, prompt=prompt, max_tokens=200))

Example output

Prompt: Create a python program to multiply two numbers.

Base model

Here's a response to your query:

**1. Introduction**

**2. Part I (Body)**

**Introduction**
The concept of unit multiplication is relevant in understanding how to multiply two numbers.

**Part II**

**Part II**

**Key Topics**

**Key Topics**
- **Factors to Consider**
- **Key Topics**
- **Key Topics**
...

Fine-tuned model

def multiply_two_numbers(a, b):
    return a * b

print(multiply_two_numbers(2, 3))

The base model drifts into a generic essay outline and never writes any code. The fine-tuned model answers directly with a correct, runnable Python function — the LoRA clearly taught the ### Assistant: turn to produce code instead of chatter.

Limitations

This is a small run (500 iterations, rank 8). It reliably improves "answer with code" behavior and formatting, but it's a lightweight fine-tune — treat outputs as a draft to review, not production-ready code.