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

Libraries

How to use KANGYONGMA/Chemistry with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KANGYONGMA/Chemistry",
	filename="Gemma-7B.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use KANGYONGMA/Chemistry with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

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 KANGYONGMA/Chemistry
# Run inference directly in the terminal:
./llama-cli -hf KANGYONGMA/Chemistry

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 KANGYONGMA/Chemistry
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KANGYONGMA/Chemistry

Use Docker

docker model run hf.co/KANGYONGMA/Chemistry

LM Studio
Jan
Ollama
How to use KANGYONGMA/Chemistry with Ollama:
```
ollama run hf.co/KANGYONGMA/Chemistry
```

Unsloth Studio

How to use KANGYONGMA/Chemistry 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 KANGYONGMA/Chemistry 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 KANGYONGMA/Chemistry to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use KANGYONGMA/Chemistry with Docker Model Runner:
```
docker model run hf.co/KANGYONGMA/Chemistry
```

Lemonade

How to use KANGYONGMA/Chemistry with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KANGYONGMA/Chemistry

Run and chat with the model

lemonade run user.Chemistry-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

AI Agents in Chemical Research: GVIM - An Intelligent Research Assistant System 🧪🤖

English | 简体中文

An intelligent research assistant system designed specifically for chemical science, featuring fine-tuned language models and specialized chemistry capabilities.

🌟 Highlights

🧬 Core Features

Fine-tuned LLMs for chemistry
Molecular visualization
Literature retrieval & analysis
Multimodal capabilities

🚀 Key Benefits

Specialized for chemical research
Continuous learning ability
Free GPU resources on Colab
Comprehensive documentation

📚 Model Overview

GVIM combines several cutting-edge technologies:

🔬 Technical Features

Fine-tuned Models: Trained on curated chemistry instruction data
Chemistry Tools: Molecular visualization and SMILES processing
Smart Retrieval: Advanced chemical literature search and analysis
Multimodal Support: Formula recognition and image analysis
Knowledge Base: Local document processing and continuous learning

🎯 Main Applications

Chemical research assistance and analysis
Molecular structure visualization
Literature review and knowledge extraction
Handwritten formula recognition
Document-based knowledge processing

💻 Quick Start

Requirements

# Create conda environment
conda create -n gvim python=3.9.19
conda activate gvim

# Install dependencies
pip install -r requirements.txt

API Configuration ⚙️

Required API keys:

TAVILY_API_KEY="your_key_here"
REPLICATE_API_TOKEN="your_token_here"
Groq_API_KEY="your_key_here"

Launch 🚀

python app.py

🎥 Key Features Showcase

📸 Click to view feature demonstrations

1. Nature Chemistry Search Interface

2. Multimodal Chemical Recognition

3. Local Document Analysis

🆓 Free GPU Resources

Access GVIM's capabilities using Colab's free GPU resources:

📖 Citation

@article{Digital Discovery,
  author = {Kangyong Ma},
  affiliation = {College of Physics and Electronic Information Engineering, Zhejiang Normal University},
  address = {Jinhua City, 321000, China},
  doi = {10.1039/D4DD00398E},
  email = {kangyongma@outlook.com, kangyongma@gmail.com}
}

📞 Contact

📧 Email

🏢 Institution

College of Physics and Electronic Information Engineering
Zhejiang Normal University
Jinhua City, 321000, China

🌟 Star us on GitHub | 📌 Commercial use requires authorization | 📑 Read our paper

Downloads last month: 79

GGUF

Model size

9B params

Architecture

gemma

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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

KANGYONGMA
/

Chemistry