Instructions to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL")
model = AutoModelForCausalLM.from_pretrained("mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL 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 mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0
# Run inference directly in the terminal:
llama cli -hf mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0
# Run inference directly in the terminal:
llama cli -hf mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_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 mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_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 mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0

Use Docker

docker model run hf.co/mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0

LM Studio
Jan

vLLM

How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0

SGLang

How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with Ollama:
```
ollama run hf.co/mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with Docker Model Runner:
```
docker model run hf.co/mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0
```

Lemonade

How to use mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mlx-community/Mistral-7B-v0.1-LoRA-Text2SQL:Q8_0

Run and chat with the model

lemonade run user.Mistral-7B-v0.1-LoRA-Text2SQL-Q8_0

List all available models

lemonade list

Atomic Chat

Mistral-7B-v0.1-LoRA-Text2SQL / tokenizer.model

Commit History

Fine tuning Text2SQL based on Mistral-7B using LoRA on MLX

e3678d1

军舰 commited on Jan 25, 2024

Commit History

Fine tuning Text2SQL based on Mistral-7B using LoRA on MLX e3678d1

Fine tuning Text2SQL based on Mistral-7B using LoRA on MLX

e3678d1