Update README.md

README.md

@@ -1,202 +1,153 @@
 ---
 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 Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for SicMundus
 
 ## Model Details
 
 ### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+This model, **SicMundus**, 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
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
+- General-purpose instruction-following tasks
+- Text generation
+- Code generation assistance
+- Conversational AI applications
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+### Downstream Use
+- Further fine-tuning on domain-specific datasets
+- Deployment in chatbot applications
+- Text summarization or document completion
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- 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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+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.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Get Started with the Model
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+To use the fine-tuned model, follow these steps:
 
-## How to Get Started with the Model
+```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")
 
-Use the code below to get started with the model.
+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)
 
-[More Information Needed]
+prompt = "Explain the concept of reinforcement learning."
+print(generate_response(prompt))
+```
 
 ## Training Details
 
 ### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+- **Dataset:** `garage-bAInd/Open-Platypus`
+- **Preprocessing:** The dataset was formatted using Alpaca-style prompts with instruction, input, and output fields.
 
 ### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **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
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **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
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+## 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)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
-
-[More Information Needed]
+- Transformer-based architecture (Llama-3.2-1B)
+- Instruction-following optimization with PEFT-LoRA
 
 ### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.14.0
+- **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