---
base_model: unsloth/Qwen3.5-2B
datasets:
- theprint/Alpaca-Docs-n-Summaries
tags:
- fine-tuned
- lora
- sft
- auto-sft
language:
- en
library_name: transformers
---

# Summarizer-v1-2B

A fine-tuned version of [`unsloth/Qwen3.5-2B`](https://huggingface.co/unsloth/Qwen3.5-2B) trained on **theprint Alpaca Docs n Summaries** data using Auto-SFT — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the `theprint Alpaca Docs n Summaries` dataset. Expect improved performance on tasks similar to those represented in the training data.

## Model Details

| Property | Value |
|---|---|
| Base model | `unsloth/Qwen3.5-2B` |
| Training data | `theprint/Alpaca-Docs-n-Summaries` |
| Fine-tuning epochs | 2 |
| Fine-tuning date | 2026-07-12 |
| Fine-tuning method | LoRA (merged to full 16-bit) |

## Training Hyperparameters

### LoRA

| Parameter | Value |
|---|---|
| `r` | `64` |
| `alpha` | `64` |
| `dropout` | `0.0` |
| `target_modules` | `['q_proj', 'v_proj', 'k_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']` |

### Training

| Parameter | Value |
|---|---|
| `learning_rate` | `1e-05` |
| `batch_size` | `4` |
| `gradient_accumulation_steps` | `1` |
| `warmup_ratio` | `0.05` |
| `max_seq_length` | `2048` |
| `quantization` | `none` |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model     = AutoModelForCausalLM.from_pretrained("theprint/Summarizer-v1-2B")
tokenizer = AutoTokenizer.from_pretrained("theprint/Summarizer-v1-2B")
```

---
*Generated by Auto-SFT*