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Leo3.2-0.5B

Text Generation
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qwen2
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conversational
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Leo 3.2 - 0.5B Parameter Language Model

🦁 Leo 3.2: A Fine-Tuned Conversational AI

Developed by Synaptom | Founded by Joniethanel F. Babor

📋 Model Overview

Leo 3.2 (Leo3.2-0.5B) is a specialized fine-tuned version of Qwen2.5-0.5B-Instruct, developed by Synaptom. This model has been carefully trained to have its own unique identity, adaptive personality, and strong conversational capabilities.

Key Information

  • Model Name: Leo3.2-0.5B
  • Version: 3.2
  • Parameters: 0.5 Billion (500M)
  • Base Model: Qwen/Qwen2.5-0.5B-Instruct (Alibaba Cloud)
  • Developer: Synaptom
  • Founder: Joniethanel F. Babor
  • Fine-tuning Method: LoRA with Unsloth
  • Training Hardware: Google Colab T4 GPU
  • License: Apache 2.0
  • Language: English

✨ Key Features

🎭 Adaptive Personality

  • Casual Mode: Friendly, fun, and engaging with emojis for everyday conversations
  • Professional Mode: Serious, focused, and formal for important tasks (work, essays, emergencies)
  • Automatically switches tone based on context

🎯 Strong Identity

  • Firmly identifies as "Leo 3.2" or "Leo3.2-0.5B"
  • Resists being called other model names (Qwen, ChatGPT, Claude, etc.)
  • Clear understanding of its development history

👨‍💻 Creator Recognition

  • Knows it was fine-tuned by Synaptom
  • Recognizes Joniethanel F. Babor as Synaptom's founder
  • Understands its relationship with Qwen2.5 base model and Alibaba Cloud

💪 Capabilities

  • Natural conversational abilities
  • Professional writing (emails, essays, reports)
  • Homework and study assistance
  • Creative content generation
  • Problem-solving and brainstorming
  • Code understanding and generation

🚀 Quick Start

Using Transformers 

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "Synaptom/Leo3.2-0.5B",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Synaptom/Leo3.2-0.5B")

# Chat with Leo
messages = [
    {"role": "user", "content": "What's your name?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Using GGUF (for llama.cpp, Ollama, LM Studio) 

# Download your preferred quantization
# Q4_K_M - Recommended (balanced quality/size)
# Q5_K_M - Higher quality
# Q8_0 - Highest quality
# F16 - Full precision

# Example with llama.cpp
./llama-cli -m leo3.2-0.5B-Q4_K_M.gguf -p "What's your name?" -n 128

📦 Available Formats

PyTorch Model

  • Full precision merged model ready for inference
  • Compatible with Transformers library
  • Optimized for deployment

GGUF Quantizations

File Quantization Size Use Case
leo3.2-0.5B-Q4_K_M.gguf Q4_K_M ~0.3GB ⭐ Recommended - Best balance
leo3.2-0.5B-Q5_K_M.gguf Q5_K_M ~0.4GB Higher quality
leo3.2-0.5B-Q8_0.gguf Q8_0 ~0.5GB Highest quality
leo3.2-0.5B-F16.gguf F16 ~1.0GB Full precision

🎓 Training Details

Dataset

  • 40+ carefully crafted examples covering:
    • Identity and name recognition (15+ examples)
    • Creator and developer information (8+ examples)
    • Base model context (4+ examples)
    • Resistance to other model names (5+ examples)
    • Casual/fun interactions (5+ examples)
    • Professional/serious contexts (6+ examples)
    • Mixed context understanding (4+ examples)

Hyperparameters

  • Method: LoRA (Low-Rank Adaptation)
  • LoRA Rank (r): 16
  • LoRA Alpha: 16
  • Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
  • Learning Rate: 2e-4
  • Batch Size: 2 (per device)
  • Gradient Accumulation: 4 steps
  • Max Steps: ~120
  • Optimizer: AdamW 8-bit
  • Training Framework: Unsloth (optimized for efficiency)
  • Hardware: Google Colab T4 GPU

💬 Example Conversations

Casual Conversation 

User: What's your name?
Leo: Hey there! 😊 I'm Leo, version 3.2 to be exact! You can call me Leo3.2-0.5B. Nice to meet you!

User: Tell me a joke
Leo: Sure thing! 😄 Why did the AI go to therapy? Because it had too many unresolved issues! 🤖💭

Professional Context 

User: Help me write a professional email to my boss
Leo: Absolutely, I'll help you craft a professional email. Please provide me with the following details:

1. The purpose of the email
2. Key points you need to communicate
3. Any specific tone or approach you'd like (formal, semi-formal)
4. Relevant context about the situation

Once I have this information, I'll draft a clear, professional email for you.

Identity Questions 

User: Are you Qwen?
Leo: No, I'm not Qwen! 😄 I'm Leo 3.2. While I was built using Qwen2.5 as my base model, I was fine-tuned and developed by Synaptom, so I'm my own model now - Leo!

🔧 Technical Specifications

Architecture

  • Base: Qwen2.5 architecture (Transformer-based)
  • Parameters: 500 million (0.5B)
  • Context Length: 2048 tokens
  • Vocabulary Size: ~151,000 tokens
  • Attention Heads: Multi-head attention
  • Hidden Size: Optimized for 0.5B parameters

Model Modifications

  • Fine-tuned with LoRA adapters merged into base weights
  • Enhanced identity and personality layers
  • Optimized for conversational coherence
  • Improved context switching between casual and professional tones

📊 Use Cases

✅ Recommended Use Cases

  • Personal assistant applications
  • Educational tutoring and homework help
  • Creative writing assistance
  • Professional document drafting
  • Casual conversation and entertainment
  • Code explanation and basic programming help
  • Brainstorming and ideation

⚠️ Limitations

  • Not suitable for critical decision-making without human oversight
  • May have knowledge cutoff limitations from base model
  • Limited to 0.5B parameters (less capable than larger models)
  • Best for conversational tasks rather than complex reasoning

🤝 Credits and Attribution

Development

  • Fine-tuned by: Synaptom
  • Founder: Joniethanel F. Babor
  • Base Model: Qwen2.5-0.5B-Instruct by Alibaba Cloud
  • Training Framework: Unsloth (https://github.com/unslothai/unsloth)
  • Infrastructure: Google Colab T4 GPU

Acknowledgments

Special thanks to:

  • Alibaba Cloud for the Qwen2.5 base model
  • Unsloth team for the efficient fine-tuning framework
  • HuggingFace for model hosting and distribution

📄 License

This model is released under the Apache 2.0 License, following the base model's licensing terms.

  • ✅ Commercial use allowed
  • ✅ Modification and distribution allowed
  • ✅ Patent use allowed
  • ⚠️ Must include license and copyright notice
  • ⚠️ Must state significant changes made

🐛 Issues and Feedback

If you encounter any issues or have suggestions:

  • Open an issue on the model repository
  • Contact Synaptom through HuggingFace
  • Provide detailed feedback for continuous improvement

📚 Citation

If you use Leo 3.2 in your research or applications, please cite:

@misc{leo3.2-0.5b},
  title={{Leo 3.2: A Fine-Tuned Conversational Language Model}},
  author={{Synaptom (Joniethanel F. Babor)}},
  year={{2025}},
  publisher={{HuggingFace}},
  howpublished={\url{https://huggingface.co/Synaptom/Leo3.2-0.5B}}}
}

🌟 Updates and Versions

  • v3.2 (Current) - Initial release with 40+ training examples
    • Strong identity recognition
    • Adaptive tone switching
    • Enhanced conversational abilities

Developed with ❤️ by Synaptom

Founder: Joniethanel F. Babor

🦁 Leo 3.2 - Your Adaptive AI Assistant

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