Instructions to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf",
	filename="Matellem-Gemma3n-E2B-Graphene-F16.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16

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 Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16

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 Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16

Use Docker

docker model run hf.co/Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16

LM Studio
Jan
Ollama
How to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf with Ollama:
```
ollama run hf.co/Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16
```

Unsloth Studio

How to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-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 Shinapri/Matellem-Gemma3n-E2B-Graphene-1-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 Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf with Docker Model Runner:
```
docker model run hf.co/Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16
```

Lemonade

How to use Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shinapri/Matellem-Gemma3n-E2B-Graphene-1-gguf:F16

Run and chat with the model

lemonade run user.Matellem-Gemma3n-E2B-Graphene-1-gguf-F16

List all available models

lemonade list

Matellem-Gemma3n-E2B-Graphene-1-gguf

A fine-tuned language model, part of the Matellem project, specialized for multi-task analysis of scientific literature in the field of graphene research.

About The Project

The field of materials science, particularly research into graphene, is expanding at an incredible rate. The sheer volume of published literature makes it challenging for researchers to stay updated and find specific information efficiently.

Matellem is designed to address this challenge. This model, built upon Google's powerful and efficient gemma-3n-E4B-it, has been specifically fine-tuned to understand the complex language, nuances, and key concepts within graphene-related scientific abstracts. It serves as a specialized tool to accelerate the research process through precise data extraction, summarization, and question answering.

Model Details

Base Model: google/gemma-3n-E2B-it
Fine-tuning Data: The model was fine-tuned on a custom, high-quality dataset consisting of 2,329 question-answer pairs. This dataset was meticulously generated from 462 research paper abstracts focused on graphene.
Fine-tuning Technique: The model was trained using Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning (PEFT) method. LoRA was applied to the attention mechanism layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj) to adapt the model to the specific domain while preserving its core capabilities.
Training Configuration: Trained using bf16 precision for stability and speed, with the adamw_8bit optimizer.

Capabilities

This model is designed to perform a variety of tasks related to scientific literature analysis:

Precise Question Answering: Answering specific technical questions based on the content of a provided abstract.
Accurate Summarization: Generating concise yet comprehensive summaries of the key findings and methodologies of a paper.
Information Extraction: Identifying and extracting specific data points, such as material properties, numerical values, or synthesis methods, from unstructured text.
Semantic Retrieval: Understanding the core concepts of a research paper, enabling the identification of relevant literature from natural language descriptions.