---
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- fine-tuned
- lora
- sft
- auto-sft
language:
- en
library_name: transformers
---

# Llama3.2-3B-Explained

A fine-tuned version of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) trained on **Explained 0.41k alpaca** data using [Auto-SFT](https://github.com/your-org/auto-sft) — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the `Explained 0.41k alpaca` dataset. Expect improved performance on tasks similar to those represented in the training data.

## Model Details

| Property | Value |
|---|---|
| Base model | `meta-llama/Llama-3.2-3B-Instruct` |
| Training data | `data/Explained-0.41k-alpaca.json` |
| Fine-tuning epochs | 2 |
| Fine-tuning date | 2026-03-25 |
| Fine-tuning method | LoRA (merged to full 16-bit) |

## Training Hyperparameters

### LoRA

| Parameter | Value |
|---|---|
| `r` | `4` |
| `alpha` | `8` |
| `dropout` | `0.0` |
| `target_modules` | `['q_proj', 'v_proj', 'k_proj', 'o_proj']` |

### Training

| Parameter | Value |
|---|---|
| `learning_rate` | `1e-05` |
| `batch_size` | `1` |
| `gradient_accumulation_steps` | `2` |
| `warmup_ratio` | `0.0` |
| `max_seq_length` | `512` |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model     = AutoModelForCausalLM.from_pretrained("theprint/Llama3.2-3B-Explained")
tokenizer = AutoTokenizer.from_pretrained("theprint/Llama3.2-3B-Explained")
```

---
*Generated by [Auto-SFT](https://github.com/your-org/auto-sft)*