Update README.md

README.md

@@ -1,202 +1,81 @@
 ---
 base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
 library_name: peft
+license: apache-2.0
+datasets:
+- ragul2607/history-llm
+language:
+- en
+tags:
+- HISTORY
+- INSTRUCTION
+- TAMIL NADU SSLC
+- LLM
+- FINE-TUNING
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for SicMundus
 
 ## Model Details
 
 ### Model Description
+**SicMundus** is a fine-tuned version of `unsloth/Llama-3.2-1B-Instruct`, optimized for historical instruction-following tasks, particularly those aligned with Tamil Nadu State Board-style history education. Using PEFT with LoRA, it has been trained on the `ragul2607/history-llm` dataset. The goal is to deliver domain-specific, accurate, and relevant historical responses.
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+- **Developed by:** Ragul
+- **Funded by:** Self-funded
+- **Organization:** Pinnacle Organization
+- **Shared by:** Ragul
+- **Model type:** Instruction-tuned Language Model (History)
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Fine-tuned from:** `unsloth/Llama-3.2-1B-Instruct`
 
-- **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]
+### Model Sources
+- **Model Repository:** [https://huggingface.co/ragul2607/SicMundus]
+- **Dataset:** [https://huggingface.co/datasets/ragul2607/history-llm]
 
 ## 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
+- Answering history questions (school/competitive level)
+- Explaining historical events, causes, impacts
+- Preparing students for TN SSLC exams
+- Educational support for teachers and learners
 
-<!-- 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
+- Fine-tuning for regional curriculums (e.g., CBSE, ICSE)
+- History-focused edtech solutions
+- AI-based tutoring and exam practice tools
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- General programming, math, or science tasks
+- Legal, financial, or medical advice
+- Real-time decision-critical systems
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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]
-
-### 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]
-
-## 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]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### 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. -->
+Since the model is trained on curated historical Q&A, it may exhibit dataset-induced biases or regional perspectives. It is not intended to be used as a definitive authority on history, especially for critical or controversial events.
 
-[More Information Needed]
+**Recommendation:** Always cross-check with textbooks or official curriculum content.
 
-## More Information [optional]
+## Getting Started
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-## Model Card Authors [optional]
+model_path = "ragul2607/SicMundus"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")
 
-[More Information Needed]
+prompt = """Below is an input followed by its expected output. Complete the task appropriately.
 
-## Model Card Contact
+### Input:
+Explain the causes of the French Revolution.
 
-[More Information Needed]
-### Framework versions
+### Output:
+"""
 
-- PEFT 0.14.0
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))