Instructions to use thongbuind/SAI-for-VinFast-FP16_100M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use thongbuind/SAI-for-VinFast-FP16_100M 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 thongbuind/SAI-for-VinFast-FP16_100M
# Run inference directly in the terminal:
llama cli -hf thongbuind/SAI-for-VinFast-FP16_100M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thongbuind/SAI-for-VinFast-FP16_100M
# Run inference directly in the terminal:
llama cli -hf thongbuind/SAI-for-VinFast-FP16_100M

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 thongbuind/SAI-for-VinFast-FP16_100M
# Run inference directly in the terminal:
./llama-cli -hf thongbuind/SAI-for-VinFast-FP16_100M

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 thongbuind/SAI-for-VinFast-FP16_100M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thongbuind/SAI-for-VinFast-FP16_100M

Use Docker

docker model run hf.co/thongbuind/SAI-for-VinFast-FP16_100M

Llama new
LM Studio
Jan

vLLM

How to use thongbuind/SAI-for-VinFast-FP16_100M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thongbuind/SAI-for-VinFast-FP16_100M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thongbuind/SAI-for-VinFast-FP16_100M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thongbuind/SAI-for-VinFast-FP16_100M

Ollama
How to use thongbuind/SAI-for-VinFast-FP16_100M with Ollama:
```
ollama run hf.co/thongbuind/SAI-for-VinFast-FP16_100M
```
Unsloth Desktop
Docker Model Runner
How to use thongbuind/SAI-for-VinFast-FP16_100M with Docker Model Runner:
```
docker model run hf.co/thongbuind/SAI-for-VinFast-FP16_100M
```

Lemonade

How to use thongbuind/SAI-for-VinFast-FP16_100M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thongbuind/SAI-for-VinFast-FP16_100M

Run and chat with the model

lemonade run user.SAI-for-VinFast-FP16_100M-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

SAI-for-VinFast-FP16_100M

Mô hình được fine-tune từ SAI_100M, dành riêng cho tác vụ trợ lí ảo trên xe VinFast.

Bản giảm độ chính xác xuống FP16, định dạng GGUF; norm giữ F32. File trọng số: SAI-for-VinFast-FP16_100M.gguf. Config và tokenizer đã nằm trong file GGUF, không cần tải riêng.

Dùng llama.cpp revision cb7934c52ca8710994b2ecc19775ebefcfdb8d01 và áp dụng llama-ugm-byte-fallback.patch đi kèm trước khi build runtime:

git -C /path/to/llama.cpp checkout cb7934c52ca8710994b2ecc19775ebefcfdb8d01
git -C /path/to/llama.cpp apply /absolute/path/to/llama-ugm-byte-fallback.patch

Sau đó build llama.cpp cho máy của bạn và load file GGUF. Dùng chat template trong GGUF, nội dung tiếng Việt được lowercase. Patch cần thiết để giữ đúng byte fallback của tokenizer SAI; llama.cpp stock/Ollama chưa được xác minh.

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GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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