| --- |
| 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 |
