Instructions to use ragul2607/SicMundus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ragul2607/SicMundus with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "ragul2607/SicMundus")

llama-cpp-python

How to use ragul2607/SicMundus with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ragul2607/SicMundus",
	filename="unsloth.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ragul2607/SicMundus with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ragul2607/SicMundus:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ragul2607/SicMundus:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ragul2607/SicMundus:Q8_0
# Run inference directly in the terminal:
llama-cli -hf ragul2607/SicMundus:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ragul2607/SicMundus:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ragul2607/SicMundus:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ragul2607/SicMundus:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ragul2607/SicMundus:Q8_0

Use Docker

docker model run hf.co/ragul2607/SicMundus:Q8_0

LM Studio
Jan
Ollama
How to use ragul2607/SicMundus with Ollama:
```
ollama run hf.co/ragul2607/SicMundus:Q8_0
```

Unsloth Studio new

How to use ragul2607/SicMundus with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ragul2607/SicMundus to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ragul2607/SicMundus to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ragul2607/SicMundus to start chatting

Pi new

How to use ragul2607/SicMundus with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ragul2607/SicMundus:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ragul2607/SicMundus:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ragul2607/SicMundus with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ragul2607/SicMundus:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ragul2607/SicMundus:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use ragul2607/SicMundus with Docker Model Runner:
```
docker model run hf.co/ragul2607/SicMundus:Q8_0
```

Lemonade

How to use ragul2607/SicMundus with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ragul2607/SicMundus:Q8_0

Run and chat with the model

lemonade run user.SicMundus-Q8_0

List all available models

lemonade list

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))

Downloads last month: 9

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

ragul2607
/

SicMundus