---
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 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.

- **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`

### Model Sources
- **Model Repository:** [https://huggingface.co/ragul2607/SicMundus]
- **Dataset:** [https://huggingface.co/datasets/ragul2607/history-llm]

## Uses

### 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

### 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
- General programming, math, or science tasks
- Legal, financial, or medical advice
- Real-time decision-critical systems

## Bias, Risks, and Limitations

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.

**Recommendation:** Always cross-check with textbooks or official curriculum content.

## Getting Started

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = "ragul2607/SicMundus"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")

prompt = """Below is an input followed by its expected output. Complete the task appropriately.

### Input:
Explain the causes of the French Revolution.

### Output:
"""

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))