Instructions to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF",
	filename="gemma_7b_instruction.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_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_7B_Instruct_V1_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_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 VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_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 VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_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 VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M

Use Docker

docker model run hf.co/VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF with Ollama:
```
ollama run hf.co/VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M
```

Unsloth Studio

How to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_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_7B_Instruct_V1_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_7B_Instruct_V1_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_7B_Instruct_V1_GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF with Docker Model Runner:
```
docker model run hf.co/VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M
```

Lemonade

How to use VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MalayaLLM_Gemma_7B_Instruct_V1_GGUF-Q4_K_M

List all available models

lemonade list

MalayaLLM: Gemma [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 model.

Model type: A 7B Gemma finetuned model on Malayalam tokens.
Language(s): Malayalam and English
Datasets: CohereForAI/aya_dataset
Source Model: MalayaLLM_Gemma_7B_Base_V1
Instruct Model: MalayaLLM_Gemma_7B_Instruct_V1
Training Precision: float16
Github Repo: MalayaLLM-Gemma

Model Update

Latest Gemma2-9B trained model is here :MalayaLLM:Gemma-2-9B

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.cpp locally, follow the instructions provided in the build documentation.
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_7b_instruction.Q4_K_M.gguf -ngl 42 -c 128 -n 100
```
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

Made Using UNSLOTH

Thanks to Unsloth, the process of fine-tuning large language models (LLMs) has become much easier and more efficient. Unsloth

🌟Happy coding💻🌟

Downloads last month: 7

GGUF

Model size

9B params

Architecture

gemma

Hardware compatibility

4-bit

Inference Providers NEW

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

Collection including VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF

MalayaLLM[മലയാളം/Malayalam]: Gemma-7B

Collection

Gemma based Malayalam pretrained , finetuned models • 3 items • Updated Aug 28, 2024 • 2

MalayaLLM: Gemma [മലയാളം/Malayalam]

Introducing the Developer:

Model description

Model Update

How to run GGUF

llama.cpp Web Server

Building llama.cpp

Running llama.cpp as a Web Server

Accessing the Web UI

Made Using UNSLOTH

🌟Happy coding💻🌟

Collection including VishnuPJ/MalayaLLM_Gemma_7B_Instruct_V1_GGUF