Instructions to use VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_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 VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16 # Run inference directly in the terminal: llama cli -hf VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16 # Run inference directly in the terminal: llama cli -hf VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
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 VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
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 VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
Use Docker
docker model run hf.co/VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
- LM Studio
- Jan
- Ollama
How to use VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF with Ollama:
ollama run hf.co/VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
- Unsloth Studio
How to use VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_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 VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_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 VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF with Docker Model Runner:
docker model run hf.co/VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
- Lemonade
How to use VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VishnuPJ/MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF:BF16
Run and chat with the model
lemonade run user.MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF-BF16
List all available models
lemonade list
MalayaLLM: Gemma-2-2B [മലയാളം/Malayalam]
Introducing the Developer:
Discover the mind behind this model and stay updated on their contributions to the field https://www.linkedin.com/in/vishnu-prasad-j/
Model description
The MalayaLLM models have been improved and customized expanding upon the groundwork laid by the original Gemma-2-2B model.
- Model type: A 2B Gemma-2 finetuned model on Malayalam tokens.
- Language(s): Malayalam and English
- Datasets:
- Source Model: MalayaLLM_Gemma_2_2B_Base_V1.0
- Instruct Model: MalayaLLM_Gemma_2_2B_Instruct_V1.0
- GGUF Model: MalayaLLM_Gemma_2_2B_Instruct_V1.0_GGUF
- Training Precision:
float16
Old Model
Gemma-7B,9B trained model is here :MalayaLLM:Gemma-7B
How to run GGUF
llama.cpp Web Server
- The web server is a lightweight HTTP server that can be used to serve local models and easily connect them to existing clients.
Building llama.cpp
- To build
llama.cpplocally, follow the instructions provided in the build documentation.
- To build
Running llama.cpp as a Web Server
- Once you have built
llama.cpp, you can run it as a web server. Below is an example of how to start the server:llama-server.exe -m gemma_2_9b_instruction.Q4_K_M.gguf -ngl 42 -c 128 -n 100
- Once you have built
Accessing the Web UI
- After starting the server, you can access the basic web UI via your browser at the following address:
http://localhost:8080

- After starting the server, you can access the basic web UI via your browser at the following address:
http://localhost:8080
Made Using UNSLOTH
Thanks to Unsloth, the process of fine-tuning large language models (LLMs) has become much easier and more efficient.
🌟Happy coding💻🌟
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