Qwen2.5-0.5B-Capybara

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the 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 (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 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

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:

@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 for the base model
• TRL Library for the training framework
• Capybara Dataset creators