ragul2607/Pinnacle

Model Card for SicMundus

Model Details

Model Description

This model, Pinnacle, is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct utilizing Parameter Efficient Fine-Tuning (PEFT) with LoRA (Low-Rank Adaptation). It has been trained on the Open-Platypus dataset with a structured Alpaca-style prompt format. The primary goal is to enhance instruction-following capabilities while maintaining efficiency through 4-bit quantization.

• Developed by: Ragul
• Funded by: Self-funded
• Organization: Pinnacle Organization
• Shared by: Ragul
• Model type: Instruction-tuned Language Model
• Language(s) (NLP): English
• License: Apache 2.0 (or specify if different)
• Finetuned from model: unsloth/Llama-3.2-1B-Instruct

Model Sources

• Repository: [https://huggingface.co/ragul2607/SicMundus]
• Paper: N/A (or link to relevant research)
• Demo: [Gradio, HF Spaces, etc.]

Uses

Direct Use

• General-purpose instruction-following tasks
• Text generation
• Code generation assistance
• Conversational AI applications

Downstream Use

• Further fine-tuning on domain-specific datasets
• Deployment in chatbot applications
• Text summarization or document completion

Out-of-Scope Use

• Not designed for real-time critical applications (e.g., medical or legal advice)
• May not be suitable for handling highly sensitive data

Bias, Risks, and Limitations

While the model is designed to be a general-purpose assistant, it inherits biases from the pre-trained Llama model and the Open-Platypus dataset. Users should be aware of potential biases in generated responses, particularly regarding sensitive topics.

Recommendations

• Use in conjunction with human oversight.
• Avoid deploying in high-stakes scenarios without additional testing.

How to Get Started with the Model

To use the fine-tuned model, follow these steps:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "path/to/SicMundus"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")

def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    output = model.generate(**inputs, max_new_tokens=100)
    return tokenizer.decode(output[0], skip_special_tokens=True)

prompt = "Explain the concept of reinforcement learning."
print(generate_response(prompt))

Training Details

Training Data

• Dataset: garage-bAInd/Open-Platypus
• Preprocessing: The dataset was formatted using Alpaca-style prompts with instruction, input, and output fields.

Training Procedure

• Training Framework: Hugging Face transformers + trl (PEFT + LoRA)
• Precision: Mixed precision (FP16/BF16 based on hardware support)
• Batch size: 2 per device with gradient accumulation
• Learning rate: 2e-4
• Max Steps: 100
• Optimizer: AdamW 8-bit
• LoRA Config: Applied to key transformer layers (q_proj, k_proj, v_proj, etc.)

Speeds, Sizes, Times

• Checkpoint Size: ~2GB (LoRA adapters stored separately)
• Fine-tuning Time: ~1 hour on A100 GPU

Evaluation

Testing Data, Factors & Metrics

• Testing Data: A subset of Open-Platypus
• Factors: Performance on general instruction-following tasks
• Metrics:
  • Perplexity (PPL)
  • Response Coherence
  • Instruction-following accuracy

Results

• Perplexity: TBD
• Response Quality: Qualitatively improved over base model on test prompts

Model Examination

• Interpretability: Standard transformer-based behavior with LoRA fine-tuning.
• Explainability: Outputs can be analyzed with attention visualization tools.

Environmental Impact

• Hardware Type: A100 GPU
• Hours used: ~1 hour
• Cloud Provider: Local GPU / AWS / Hugging Face Accelerate
• Carbon Emitted: Estimated using Machine Learning Impact Calculator

Technical Specifications

Model Architecture and Objective

• Transformer-based architecture (Llama-3.2-1B)
• Instruction-following optimization with PEFT-LoRA

Compute Infrastructure

• Hardware: A100 (or specify if different)
• Software: Python, PyTorch, transformers, unsloth, peft

Citation

If using this model, please cite:

@misc{SicMundus,
  author = {Ragul},
  title = {SicMundus: Fine-Tuned Llama-3.2-1B-Instruct},
  year = {2025},
  url = {https://huggingface.co/ragul2607/SicMundus}
}

More Information

• Contact: [https://github.com/ragultv]
• Further Work: Integrate with RLHF for better alignment

Model Card Authors

• Ragul