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

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))