Instructions to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_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_7B_Instruct_v0.1_GGUF
# Run inference directly in the terminal:
llama cli -hf VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF
# Run inference directly in the terminal:
llama cli -hf VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

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_7B_Instruct_v0.1_GGUF
# Run inference directly in the terminal:
./llama-cli -hf VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

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_7B_Instruct_v0.1_GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

Use Docker

docker model run hf.co/VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

LM Studio
Jan

vLLM

How to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

Ollama
How to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF with Ollama:
```
ollama run hf.co/VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF
```

Unsloth Studio

How to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_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 VishnuPJ/MalayaLLM_7B_Instruct_v0.1_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 VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF with Docker Model Runner:
```
docker model run hf.co/VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF
```

Lemonade

How to use VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VishnuPJ/MalayaLLM_7B_Instruct_v0.1_GGUF

Run and chat with the model

lemonade run user.MalayaLLM_7B_Instruct_v0.1_GGUF-{{QUANT_TAG}}

List all available models

lemonade list

VishnuPJ commited on Jan 31, 2024

Commit

a5f86dc

verified ·

1 Parent(s): 8f821f0

Update README.md

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@@ -10,6 +10,9 @@ pipeline_tag: text-generation
 <img src="https://cdn-uploads.huggingface.co/production/uploads/64e65800e44b2668a56f9731/bipVMulaNJ9um46ecYpR4.png" alt="Baby MalayaLLM" width="300" height="200">
 # MalayaLLM_7B_Instruct_v0.1_GGUF
 This is an attempt to construct a Language Model (LLM) focused on **generative AI for Malayalam language**. While several LLMs are proficient in supporting multiple languages, including Malayalam, enhancing their performance for specific tasks such as content generation and question answering specifically in Malayalam can be achieved through dedicated training on a Malayalam dataset. In pursuit of this, I've undertaken the **continuous pre-training of the LLAMA2 model using a comprehensive Malayalam dataset**.

 <img src="https://cdn-uploads.huggingface.co/production/uploads/64e65800e44b2668a56f9731/bipVMulaNJ9um46ecYpR4.png" alt="Baby MalayaLLM" width="300" height="200">
+- GGUF version of  MalayaLLM_7B_Instruct_v0.1_GGUF
+- 8 bit quantized model.
 # MalayaLLM_7B_Instruct_v0.1_GGUF
 This is an attempt to construct a Language Model (LLM) focused on **generative AI for Malayalam language**. While several LLMs are proficient in supporting multiple languages, including Malayalam, enhancing their performance for specific tasks such as content generation and question answering specifically in Malayalam can be achieved through dedicated training on a Malayalam dataset. In pursuit of this, I've undertaken the **continuous pre-training of the LLAMA2 model using a comprehensive Malayalam dataset**.