Instructions to use rajasingh012/vidya-gemma4-e2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rajasingh012/vidya-gemma4-e2b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rajasingh012/vidya-gemma4-e2b-gguf",
	filename="ncert-socratic-e2b-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rajasingh012/vidya-gemma4-e2b-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M

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 rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M

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 rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M

Use Docker

docker model run hf.co/rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use rajasingh012/vidya-gemma4-e2b-gguf with Ollama:
```
ollama run hf.co/rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M
```

Unsloth Studio

How to use rajasingh012/vidya-gemma4-e2b-gguf 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 rajasingh012/vidya-gemma4-e2b-gguf 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 rajasingh012/vidya-gemma4-e2b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rajasingh012/vidya-gemma4-e2b-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use rajasingh012/vidya-gemma4-e2b-gguf with Docker Model Runner:
```
docker model run hf.co/rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M
```

Lemonade

How to use rajasingh012/vidya-gemma4-e2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rajasingh012/vidya-gemma4-e2b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.vidya-gemma4-e2b-gguf-Q4_K_M

List all available models

lemonade list

vidya-gemma4-e2b-gguf / README.md

rajasingh012

Upload README.md with huggingface_hub

1eb2d5a verified about 2 months ago

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metadata

base_model: unsloth/gemma-4-E2B-it-unsloth-bnb-4bit
model:
  - id: rajasingh012/vidya-gemma4-e2b-gguf
tags:
  - unsloth
  - fine-tuned
  - gemma-4
  - ncert
  - socratic-tutoring
  - education
  - llm
library: llama.cpp
quantization: Q4_K_M
inference:
  parameters:
    temperature: 0.7
    top_k: 40
    top_p: 0.95

Vidya - NCERT Socratic Learning Bot

Fine-tuned Gemma 4 E2B (2.3B params) on NCERT socratic dialogue data.

Model Details

Base model: unsloth/gemma-4-E2B-it-unsloth-bnb-4bit
Fine-tuning method: QLoRA (rank=32, alpha=32)
Format: GGUF Q4_K_M (~3.1 GB)
Context length: 2048 tokens
Trained on: 321 socratic dialogue samples (NCERT Class 6-10 Science)

Usage

This model is compatible with llama.cpp and Ollama. Download the GGUF file from the Files tab, or use the HuggingFace Hub API:

from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id='rajasingh012/vidya-gemma4-e2b-gguf', 
                       filename='gemma-4-e2b-it-pretrained.Q4_K_M.gguf')

Mobile App

This model powers the Vidya Android app (NCERT Socratic tutoring chatbot). See: https://github.com/rajasingh012/vidya-android

Training

Trained via Unsloth FastModel API on Kaggle GPU (T4 x2). Full training logs and data pipeline available in the ncert-wiki repository.