Instructions to use vilm/vinallama-2.7b-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vilm/vinallama-2.7b-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vilm/vinallama-2.7b-chat-GGUF",
	filename="vinallama-2.7b-chat_q5_0.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 vilm/vinallama-2.7b-chat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vilm/vinallama-2.7b-chat-GGUF:Q5_0
# Run inference directly in the terminal:
llama-cli -hf vilm/vinallama-2.7b-chat-GGUF:Q5_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vilm/vinallama-2.7b-chat-GGUF:Q5_0
# Run inference directly in the terminal:
llama-cli -hf vilm/vinallama-2.7b-chat-GGUF:Q5_0

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 vilm/vinallama-2.7b-chat-GGUF:Q5_0
# Run inference directly in the terminal:
./llama-cli -hf vilm/vinallama-2.7b-chat-GGUF:Q5_0

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 vilm/vinallama-2.7b-chat-GGUF:Q5_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vilm/vinallama-2.7b-chat-GGUF:Q5_0

Use Docker

docker model run hf.co/vilm/vinallama-2.7b-chat-GGUF:Q5_0

LM Studio
Jan
Ollama
How to use vilm/vinallama-2.7b-chat-GGUF with Ollama:
```
ollama run hf.co/vilm/vinallama-2.7b-chat-GGUF:Q5_0
```

Unsloth Studio new

How to use vilm/vinallama-2.7b-chat-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 vilm/vinallama-2.7b-chat-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 vilm/vinallama-2.7b-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vilm/vinallama-2.7b-chat-GGUF to start chatting

Docker Model Runner
How to use vilm/vinallama-2.7b-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/vilm/vinallama-2.7b-chat-GGUF:Q5_0
```

Lemonade

How to use vilm/vinallama-2.7b-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vilm/vinallama-2.7b-chat-GGUF:Q5_0

Run and chat with the model

lemonade run user.vinallama-2.7b-chat-GGUF-Q5_0

List all available models

lemonade list

VinaLLaMA - State-of-the-art Vietnamese LLMs

Read our Paper

Prompt Format (ChatML):

<|im_start|>system
Bạn là một trợ lí AI hữu ích. Hãy trả lời người dùng một cách chính xác.
<|im_end|>
<|im_start|>user
Hello world!<|im_end|>
<|im_start|>assistant

Downloads last month: 21

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

5-bit

Inference Providers NEW

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

Space using vilm/vinallama-2.7b-chat-GGUF 1

Collection including vilm/vinallama-2.7b-chat-GGUF

VinaLLaMA

Collection

Second Generation, Most Powerful Open-Source Vietnamese LLMs. • 8 items • Updated Feb 9, 2024 • 13

Paper for vilm/vinallama-2.7b-chat-GGUF

VinaLLaMA: LLaMA-based Vietnamese Foundation Model

Paper • 2312.11011 • Published Dec 18, 2023 • 23