adityakum667388/lumichats-v1.1

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LumiChats v1.1

A Fine-tuned Conversational AI Model Based on Llama 3.2 3B

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📖 Overview

LumiChats v1.1 is a specialized conversational AI model built on top of Meta's Llama 3.2 3B Instruct foundation. This model has been fine-tuned using LoRA (Low-Rank Adaptation) with the Unsloth framework to deliver enhanced conversational capabilities while maintaining exceptional efficiency and performance.

Base Model: unsloth/Llama-3.2-3B-Instruct Model Type: Conversational AI / Instruction-tuned Language Model Parameters: 3.21 Billion (3,237,063,680 total) Trainable Parameters: 24,313,856 (~0.75% via LoRA) Architecture: Optimized Transformer with Auto-regressive Language Modeling

✨ Key Features

• —💬 Enhanced Conversational Abilities: Fine-tuned on FineTome-100k for natural, engaging dialogue
• —🚀 Efficient & Fast:
• —2x faster training and inference with Unsloth optimizations
• —4-bit quantization for reduced memory footprint
• —Only 0.75% of parameters trained via LoRA
• —🌍 Multilingual Support: Supports 8+ languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai)
• —📱 Edge-Ready: Optimized for deployment on edge devices and mobile platforms
• —🎯 Superior Instruction Following: Specialized training on response-only objectives
• —🔒 Privacy-Focused: Can run entirely on-device without cloud dependencies
• —⚡ Memory Efficient: Trained with just 2.35 GB peak memory using gradient checkpointing

🏗️ Architecture Details

LumiChats v1.1 inherits the robust architecture of Llama 3.2 3B:

• —Model Type: Auto-regressive transformer language model (LlamaForCausalLM)
• —Training Approach:
• —Base: Supervised Fine-Tuning (SFT) + Reinforcement Learning with Human Feedback (RLHF)
• —Fine-tuning: LoRA adapters with response-only training
• —Context Length: Up to 128,000 tokens (trained with maxseqlength: 2048)
• —Vocabulary Size: Extended multilingual tokenizer
• —Optimization: 4-bit quantization, structured pruning, and knowledge distillation

LoRA Configuration Details

• —LoRA Rank (r): 16
• —LoRA Alpha: 16
• —Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• —LoRA Dropout: 0
• —Trainable Parameters: 24,313,856 (0.75% of total 3.2B parameters)

🎯 Intended Use Cases

LumiChats v1.1 excels at:

• —Conversational AI: Natural dialogue and chat applications
• —Personal Assistants: Task management and information retrieval
• —Content Generation: Writing assistance and creative text generation
• —Summarization: Document and conversation summarization
• —Question Answering: Knowledge retrieval and Q&A systems
• —Code Assistance: Basic coding help and explanations
• —On-Device Applications: Mobile AI assistants and offline chatbots

🚀 Quick Start

Using Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "adityakum667388/lumichats-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Prepare conversation
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "What is the capital of France?"}
]

# Generate response
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

Using Unsloth for Inference (Fastest)

from unsloth import FastLanguageModel

# Load model with Unsloth (2x faster inference)
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="adityakum667388/lumichats-v1.1",
    max_seq_length=2048,
    dtype=None,  # Auto-detect
    load_in_4bit=True,  # Memory efficient
)

# Enable native 2x faster inference
FastLanguageModel.for_inference(model)

# Chat template
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain quantum computing"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=128,
    temperature=1.5,
    min_p=0.1
)
print(tokenizer.batch_decode(outputs))

Chat Template Format

LumiChats v1.1 uses the Llama 3.1 chat template format:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a helpful AI assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Special Tokens:

• —<|begin_of_text|> - Beginning of sequence
• —<|start_header_id|> - Start of role header
• —<|end_header_id|> - End of role header
• —<|eot_id|> - End of turn
• —<|finetune_right_pad_id|> - Padding token

Using GGUF Format (llama.cpp)

from llama_cpp import Llama

# Load GGUF model
llm = Llama(
    model_path="lumichats-v1.1-Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1  # Use GPU acceleration
)

# Format prompt with chat template
prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a helpful AI assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

What is machine learning?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

"""

# Generate response
output = llm(
    prompt,
    max_tokens=512,
    temperature=0.7,
    top_p=0.9,
    stop=["<|eot_id|>", "<|end_of_text|>", "<|im_end|>", "<|endoftext|>"]
)

print(output['choices'][0]['text'])

Using Ollama

# Pull the model (if available on Ollama)
ollama pull lumichats-v1.1

# Run inference
ollama run lumichats-v1.1 "Explain quantum computing in simple terms"

📦 Available Model Formats

Recommendation: For most users, Q4_K_M offers the best tradeoff between model size and output quality.

💻 Hardware Requirements

Minimum Requirements

• —RAM: 4 GB (for Q4KM quantized version)
• —GPU: Optional, but recommended (4GB+ VRAM)
• —Storage: 2-7 GB depending on format

Recommended Setup

• —RAM: 8 GB or more
• —GPU: NVIDIA GPU with 6GB+ VRAM (RTX 3060, T4, or better)
• —CPU: Modern multi-core processor (for CPU inference)

Performance Estimates

• —GPU (T4): 20-40 tokens/second
• —GPU (T4 with Unsloth): 40-80 tokens/second (2x faster)
• —GPU (RTX 4090): 60-100+ tokens/second
• —CPU (High-end): 5-15 tokens/second

🎨 Training Details

Training Configuration

LumiChats v1.1 was fine-tuned with the following setup:

Framework & Optimization:

• —Base Model: unsloth/Llama-3.2-3B-Instruct
• —Training Framework: Unsloth 2026.1.4 (optimized fine-tuning)
• —Fine-tuning Method: LoRA (Low-Rank Adaptation)
• —Quantization: 4-bit during training (load_in_4bit=True)
• —Gradient Checkpointing: Unsloth-optimized for memory efficiency

Dataset & Preprocessing:

• —Dataset: mlabonne/FineTome-100k
• —Format: ShareGPT → HuggingFace chat format
• —Chat Template: Llama 3.1 template
• —Training Objective: Response-only training (masks user inputs)

Hardware & Performance:

• —GPU: Tesla T4 (Max memory: 14.741 GB)
• —Peak Memory Usage: 2.35 GB additional for training
• —Training Time: 8.54 minutes (512 seconds) for 60 steps
• —Speed: 2x faster than standard PyTorch training

Training Hyperparameters

training_config = {
    "per_device_train_batch_size": 2,
    "gradient_accumulation_steps": 4,
    "effective_batch_size": 8,
    "warmup_steps": 5,
    "max_steps": 60,
    "learning_rate": 2e-4,
    "optimizer": "adamw_8bit",
    "weight_decay": 0.001,
    "lr_scheduler_type": "linear",
    "max_seq_length": 2048,
    "dtype": "float16",
    "seed": 3407
}

Why This Approach is Superior

1. 1.Efficiency: Only 0.75% of parameters trained, reducing computational cost by 99%+
2. 2.Speed: Unsloth optimizations provide 2x faster training and inference
3. 3.Memory: 4-bit quantization + gradient checkpointing enables training on consumer GPUs
4. 4.Quality: Response-only training focuses learning on generating high-quality outputs
5. 5.Versatility: Multiple export formats (HuggingFace, GGUF) for diverse deployment scenarios

The model builds upon Llama 3.2's foundation, which was pretrained on up to 9 trillion tokens from publicly available sources and further refined through supervised fine-tuning and RLHF alignment.

📊 Performance & Benchmarks

LumiChats v1.1 inherits the strong performance characteristics of Llama 3.2 3B, with enhanced conversational abilities:

• —MMLU (Massive Multitask Language Understanding): Competitive performance
• —AGIEval (General AI evaluation): Strong reasoning capabilities
• —ARC-Challenge (Abstract reasoning): Improved over base model
• —Instruction Following: Superior response quality on FineTome-100k
• —Multilingual dialogue tasks: Consistent across 8+ languages
• —Conversational Quality: Enhanced coherence and context awareness

The model outperforms similar-sized models like Gemma 2 2.6B and Phi 3.5-mini on instruction following, summarization, and conversational tasks, while maintaining efficiency advantages through LoRA and quantization.

🌐 Supported Languages

Official support for 8 languages:

• —🇬🇧 English
• —🇩🇪 German
• —🇫🇷 French
• —🇮🇹 Italian
• —🇵🇹 Portuguese
• —🇮🇳 Hindi
• —🇪🇸 Spanish
• —🇹🇭 Thai

Note: The model has been trained on additional languages and can be fine-tuned for other languages as needed.

⚖️ Limitations & Considerations

• —Context Understanding: May struggle with very long contexts despite 128k token capacity
• —Factual Accuracy: Can occasionally generate plausible but incorrect information
• —Bias: May reflect biases present in training data
• —Specialized Knowledge: Not optimized for highly technical or domain-specific tasks
• —Real-time Information: No access to current events (knowledge cutoff applies)
• —Safety: Should be deployed with appropriate content filtering and monitoring
• —LoRA Constraints: Trained parameters limited to attention and MLP layers

🔒 Responsible AI & Safety

LumiChats v1.1 is built on Llama 3.2's safety foundations:

• —Trained with safety alignment through RLHF (base model)
• —Designed to decline harmful requests
• —Tested for bias and fairness across languages
• —Implements content filtering guidelines
• —Response-only training reduces risk of prompt injection

Developers should:

• —Implement additional safety layers for production use
• —Test thoroughly for their specific use case
• —Monitor outputs for quality and appropriateness
• —Follow the Llama 3.2 Acceptable Use Policy
• —Be aware that fine-tuning may affect safety properties

📜 License

This model is released under the Llama 3.2 Community License.

• —✅ Commercial use permitted
• —✅ Modification and derivative works allowed
• —✅ Distribution allowed with attribution
• —⚠️ Subject to Llama 3.2 Acceptable Use Policy

Please review the full license at: Llama 3.2 License

🙏 Acknowledgments

• —Meta AI for developing and releasing Llama 3.2
• —Unsloth AI for the efficient fine-tuning framework and optimizations
• —Maxime Labonne for the FineTome-100k dataset
• —Hugging Face for model hosting and transformers library
• —The open-source AI community for tools and support

📞 Contact & Support

• —Model Page: huggingface.co/adityakum667388/lumichats-v1.1
• —LoRA Adapters: huggingface.co/adityakum667388/lumichats-lora
• —Issues: Report bugs or request features via the Community tab
• —Creator: @adityakum667388

🔄 Version History

v1.1 (Current)

• —Initial release
• —Fine-tuned on Llama 3.2 3B Instruct with LoRA
• —Trained on FineTome-100k dataset
• —Optimized for conversational tasks
• —Multiple export formats available (SafeTensors, GGUF, LoRA adapters)
• —2x faster inference with Unsloth
• —Peak training memory: 2.35 GB on Tesla T4

📚 Citation

If you use LumiChats v1.1 in your research or applications, please cite:

@misc{lumichats2025,
  author = {Aditya Kumar},
  title = {LumiChats v1.1: A Fine-tuned Conversational AI Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/adityakum667388/lumichats-v1.1}},
  note = {Fine-tuned using Unsloth and LoRA on FineTome-100k}
}

And the base model:

@article{llama32,
  title={Llama 3.2: Advancing Efficient and Accessible AI},
  author={Meta AI},
  year={2024},
  url={https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/}
}

And Unsloth:

@software{unsloth2024,
  author = {Unsloth AI},
  title = {Unsloth: Fast and Memory-Efficient Finetuning},
  year = {2024},
  url = {https://github.com/unslothai/unsloth}
}

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Built with ❤️ using Llama 3.2 3B | Powered by Unsloth | Trained on FineTome-100k

⭐ If you find this model useful, please consider giving it a star!

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