---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
library_name: peft
license: apache-2.0
datasets:
- garage-bAInd/Open-Platypus
language:
- en
tags:
- MATH
- LEETCODE
- text-generation-inference
- SCIENCE
---

# 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:

```python
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](https://mlco2.github.io/impact)

## 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:

```bibtex
@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