owenergy/llama3-sharegpt-10k-voice-ai

Llama 3 8B - ShareGPT 10K Voice AI

This is a LoRA-finetuned version of Meta-Llama-3-8B-Instruct, trained on 10,887 high-quality conversations from the ShareGPT52K dataset.

🎯 Model Overview

• —Base Model: meta-llama/Meta-Llama-3-8B-Instruct
• —Training Method: LoRA (Low-Rank Adaptation)
• —Quantization: 4-bit (bitsandbytes)
• —Dataset: RyokoAI/ShareGPT52K
• —Training Conversations: 10,887
• —Training Steps: 2,043
• —Training Epochs: 3
• —Adapter Size: ~161 MB

📊 Training Configuration

• —Learning Rate: 2e-4
• —Batch Size: 4 (effective: 16 with gradient accumulation)
• —Gradient Accumulation Steps: 4
• —LoRA Rank (r): 16
• —LoRA Alpha: 32
• —LoRA Dropout: 0.05
• —Optimizer: pagedadamw32bit
• —Target Modules: qproj, kproj, vproj, oproj, gateproj, upproj, down_proj
• —Trainable Parameters: 41,943,040 (0.52% of total)

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3-8B-Instruct",
    device_map="auto",
    torch_dtype=torch.float16
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "owenergy/llama3-sharegpt-10k-voice-ai")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")

# Generate response
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain quantum computing in simple terms."}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

🎓 Training Data

The model was trained on 10,887 diverse conversations from ShareGPT52K, covering:

• —💻 Technical assistance and programming
• —📚 General knowledge and education
• —✍️ Creative writing and storytelling
• —🔧 Problem-solving and troubleshooting
• —💬 Natural conversation and dialogue
• —🌍 Wide range of topics and domains

✨ What Makes This Model Special

1. 1.High-Quality Data: Trained exclusively on ShareGPT conversations, known for natural and helpful responses
2. 2.Optimized for Voice AI: Designed for conversational applications with natural dialogue flow
3. 3.Efficient LoRA: Only 161MB adapter that works with the base model
4. 4.Well-Balanced: 3 full epochs ensure good learning without overfitting
5. 5.Conversational Excellence: Excels at multi-turn dialogues and context understanding

💪 Model Strengths

• —Natural, human-like conversational responses
• —Strong context retention across multiple turns
• —Helpful and informative explanations
• —Creative problem-solving approaches
• —Adaptable to various conversation styles

⚠️ Limitations

• —Requires base Llama 3 8B model (this is just the LoRA adapter)
• —Trained primarily on English conversations
• —May inherit biases present in training data
• —Requires proper chat template formatting
• —Best performance with conversational use cases

📈 Performance

The model shows improved performance over the base model on:

• —Conversational coherence
• —Response helpfulness
• —Natural dialogue flow
• —Context understanding
• —Multi-turn conversations

🛠️ Use Cases

Perfect for:

• —🎙️ Voice assistants
• —💬 Chatbots
• —📞 Customer service AI
• —🤖 Interactive AI applications
• —📱 Mobile AI assistants
• —🌐 Web-based chat interfaces

📦 Model Files

• —adapter_model.safetensors - LoRA adapter weights (~161MB)
• —adapter_config.json - Adapter configuration
• —tokenizer_config.json - Tokenizer settings
• —special_tokens_map.json - Special tokens
• —Checkpoints saved every 100 steps

🔧 Hardware Requirements

For Inference:

• —GPU: 12GB+ VRAM (with 4-bit quantization)
• —RAM: 16GB+ system memory
• —Storage: ~5GB (base model) + 161MB (adapter)

Recommended Setup:

• —GPU: RTX 3090, RTX 4090, A100, or similar
• —With 4-bit quantization: Can run on consumer GPUs

📝 Citation

@misc{llama3-sharegpt-10k-voice-ai,
  author = {owenergy},
  title = {Llama 3 8B ShareGPT 10K Voice AI},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/owenergy/llama3-sharegpt-10k-voice-ai},
  note = {LoRA finetuned on 10,887 ShareGPT conversations}
}

📄 License

This model inherits the Llama 3 license from Meta. Please review the Llama 3 License Agreement.

🙏 Acknowledgments

• —Meta for the Llama 3 base model
• —RyokoAI for the ShareGPT52K dataset
• —HuggingFace for the transformers and PEFT libraries

📧 Contact

For questions, issues, or feedback, please open an issue on HuggingFace.

Model Card by: owenergy Date: December 2025 Status: Production Ready ✅