---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
datasets:
- trl-lib/Capybara
language:
- en
tags:
- conversational
- multi-turn
- instruction-following
- reasoning
- sft
- trl
pipeline_tag: text-generation
library_name: transformers
---

# Qwen2.5-0.5B-Capybara

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset using Supervised Fine-Tuning (SFT).

## Model Description

**Qwen2.5-0.5B-Capybara** is trained on the Capybara dataset, which is a high-quality multi-turn conversational dataset emphasizing:

- **Reasoning and Logic**: Strong focus on extrapolation and logical thinking
- **Information Diversity**: Wide range of domains including STEM, pop-culture, and general knowledge
- **Multi-turn Conversations**: Average of 3+ turns per conversation with 1,000+ tokens context
- **Natural Prose**: Maintains conversational flow while exploring complex topics

The Capybara dataset was created using the Amplify-Instruct synthesis method, combining techniques from Airoboros, Evol-Instruct (WizardLM), Orca, and other high-performing datasets.

## Training Details

### Training Configuration

| Parameter | Value |
|-----------|-------|
| Base Model | Qwen/Qwen2.5-0.5B |
| Training Method | Supervised Fine-Tuning (SFT) |
| Dataset | trl-lib/Capybara (15,806 samples) |
| Epochs | 3 |
| Batch Size | 8 |
| Gradient Accumulation | 4 |
| Effective Batch Size | 32 |
| Learning Rate | 2e-5 |
| LR Scheduler | Linear |
| Precision | BF16 |
| Max Sequence Length | 1024 |
| Optimizer | AdamW (fused) |

### Memory Optimizations

- **Liger Kernel**: Enabled for ~60% VRAM reduction
- **Gradient Checkpointing**: Enabled
- **BF16 Mixed Precision**: Enabled

### Training Infrastructure

- Framework: [TRL](https://github.com/huggingface/trl) (Transformer Reinforcement Learning)
- Hardware: Single GPU (8GB VRAM)
- Training Time: ~5 minutes per step

### Training Progress

> **Note**: This is checkpoint at step 42 of 1,482 total steps (~3% of training). This is an early checkpoint from an ongoing training run.

| Metric | Value |
|--------|-------|
| Steps Completed | 42 / 1,482 |
| Training Progress | ~3% |
| Final Loss (step 42) | 1.3058 |
| Initial Loss (step 1) | 1.9030 |

## Dataset Information

The [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset contains:

- **15,806 training samples** of multi-turn conversations
- **Sources**: GPT4LLM, GOAT, EverythingLM, Know-Logic, SuperCOT, Airoboros, Dove, TheoremQA, TaskSource, General-Instruct
- **Format**: Conversation messages with user/assistant roles
- **Quality**: Aggressively filtered to remove alignment artifacts and common undesirable behaviors

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("BurnyCoder/Qwen2.5-0.5B-Capybara")
tokenizer = AutoTokenizer.from_pretrained("BurnyCoder/Qwen2.5-0.5B-Capybara")

messages = [
    {"role": "user", "content": "Explain the concept of recursion in programming."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

- This is an early checkpoint (~3% trained) and may not reflect full model capabilities
- Model size is 494M parameters - suitable for edge deployment but limited compared to larger models
- Training was conducted on a single GPU with memory optimizations

## Citation

If you use this model, please cite:

```bibtex
@misc{qwen2.5-0.5b-capybara,
  author = {BurnyCoder},
  title = {Qwen2.5-0.5B-Capybara: Multi-turn Conversational Fine-tuning},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/BurnyCoder/Qwen2.5-0.5B-Capybara}
}
```

## Acknowledgments

- [Qwen Team](https://huggingface.co/Qwen) for the base model
- [TRL Library](https://github.com/huggingface/trl) for the training framework
- [Capybara Dataset](https://huggingface.co/datasets/trl-lib/Capybara) creators