---
license: apache-2.0
language:
- fa
base_model: unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit
tags:
- gemma-3n
- unsloth
- lora
- persian
- fa
- conversational
- fine-tuned
datasets:
- mshojaei77/persian-gk-cleaned
---

# Gemma-3n-E4B LoRA - Fine-tuned for Persian Conversations

This repository contains **LoRA (Low-Rank Adaptation) adapters** for the `unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit` model, fine-tuned on a cleaned Persian conversational dataset.

This model was fine-tuned using the powerful **Unsloth** library, which enables significantly faster training and reduces memory usage, making it possible to fine-tune on consumer-grade hardware like a single Google Colab T4 GPU.

## Model Description

- **Base Model:** [unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit)
- **Language:** Persian (fa)
- **Fine-tuning Dataset:** [mshojaei77/persian-gk-cleaned](https://huggingface.co/datasets/mshojaei77/persian-gk-cleaned)
- **Framework:** [Unsloth](https://github.com/unslothai/unsloth)

The model is designed to be a helpful Persian-speaking assistant, knowledgeable in topics covered by the training dataset, including technology, general knowledge, and conversational queries.

## 🚀 How to Use

To use these LoRA adapters, you must first load the base `unsloth/gemma-3n-E4B-it` model and then apply the adapters from this repository. The Unsloth library is required for this process.

### Installation

First, ensure you have Unsloth and all necessary dependencies installed.
```bash
# Install Unsloth for Google Colab
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes
```

### Inference Code

This code snippet demonstrates how to load the base model, attach the LoRA adapters, and run inference.

```python
from unsloth import FastLanguageModel
import torch
from transformers import TextStreamer

# Your LoRA adapter repository
lora_model_name = "mshojaei77/gemma-3n-E4B-persin-lora-adaptors"

# Load the base model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = lora_model_name, # Use your LoRA repo name
    max_seq_length = 4096,
    dtype = None,
    load_in_4bit = True,
)

# Set up a text streamer for continuous output
streamer = TextStreamer(tokenizer, skip_prompt=True)

# Prepare your prompt
messages = [{
    "role": "user",
    "content": "Node.js چیه و چه کاربردی داره؟",
}]

# Format and tokenize the input
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to("cuda")

# Run generation
_ = model.generate(
    **inputs,
    max_new_tokens=256,
    streamer=streamer,
    use_cache=True,
    # Recommended Gemma-3 settings
    temperature=0.7,
    top_p=0.95,
    top_k=64,
)
```

## Training Details

The model was fine-tuned with the following configuration:

### Framework
- **Library:** Unsloth
- **Hardware:** Google Colab T4 GPU (15GB VRAM)

### Dataset
The model was trained on the `mshojaei77/persian-gk-cleaned` dataset, which is a curated version of the original `mshojaei77/persian-gk` dataset. The cleaning process involved:
1.  Removing system messages.
2.  Ensuring strict `user`/`assistant` alternating roles.
3.  Filtering out empty or malformed conversations.

### Hyperparameters
- **LoRA `r`:** 8
- **LoRA `alpha`:** 16
- **Learning Rate:** 2e-5
- **Epochs:** 1
- **Batch Size:** 2
- **Gradient Accumulation:** 4 (Effective Batch Size: 8)
- **Optimizer:** AdamW 8-bit
- **LR Scheduler:** Linear

## Citation

If you use this model, please consider citing the original creators and tools that made it possible:

**Gemma 3N:**
```
@misc{gemma3n,
  author = {The Gemma 3N team, Google},
  title = {Gemma 3N},
  year = {2024},
  howpublished = {\url{https://ai.google.dev/gemma}},
}
```

**Unsloth:**
```
@misc{unsloth,
  author = {Daniel Han and Phil Wang},
  title = {Unsloth: Llama, Mistral & Gemma 5x faster training},
  year = {2024},
  howpublished = {\url{https://github.com/unslothai/unsloth}},
}
```