Instructions to use tharun66/mistral-sql-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tharun66/mistral-sql-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tharun66/mistral-sql-gguf",
	filename="mistral_sql_q4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tharun66/mistral-sql-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tharun66/mistral-sql-gguf
# Run inference directly in the terminal:
llama-cli -hf tharun66/mistral-sql-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tharun66/mistral-sql-gguf
# Run inference directly in the terminal:
llama-cli -hf tharun66/mistral-sql-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 tharun66/mistral-sql-gguf
# Run inference directly in the terminal:
./llama-cli -hf tharun66/mistral-sql-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 tharun66/mistral-sql-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tharun66/mistral-sql-gguf

Use Docker

docker model run hf.co/tharun66/mistral-sql-gguf

LM Studio
Jan

vLLM

How to use tharun66/mistral-sql-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tharun66/mistral-sql-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tharun66/mistral-sql-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tharun66/mistral-sql-gguf

Ollama
How to use tharun66/mistral-sql-gguf with Ollama:
```
ollama run hf.co/tharun66/mistral-sql-gguf
```

Unsloth Studio new

How to use tharun66/mistral-sql-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 tharun66/mistral-sql-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 tharun66/mistral-sql-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tharun66/mistral-sql-gguf to start chatting

Pi new

How to use tharun66/mistral-sql-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tharun66/mistral-sql-gguf

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tharun66/mistral-sql-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tharun66/mistral-sql-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tharun66/mistral-sql-gguf

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tharun66/mistral-sql-gguf

Run Hermes

hermes

Docker Model Runner
How to use tharun66/mistral-sql-gguf with Docker Model Runner:
```
docker model run hf.co/tharun66/mistral-sql-gguf
```

Lemonade

How to use tharun66/mistral-sql-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tharun66/mistral-sql-gguf

Run and chat with the model

lemonade run user.mistral-sql-gguf-{{QUANT_TAG}}

List all available models

lemonade list

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Mistral-7B SQL GGUF

A GGUF-quantized version of Mistral-7B fine-tuned for SQL query generation. Optimized for CPU inference with clean SQL outputs.

Model Details

Base Model: Mistral-7B-Instruct-v0.3
Quantization: Q8_0
Context Length: 32768 tokens (default from base model)
Format: GGUF (V3 latest)
Size: 7.17 GB
Parameters: 7.25B
Architecture: Llama
Use Case: Text to SQL conversion

Usage

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# Download and setup
model_path = hf_hub_download(
    repo_id="tharun66/mistral-sql-gguf",
    filename="mistral_sql_q4.gguf"
)

# Initialize model
llm = Llama(
    model_path=model_path,
    n_ctx=512,
    n_threads=4,
    verbose=False
)

def generate_sql(question):
    prompt = f"""### Task: Convert to SQL
### Question: {question}
### SQL:"""
    
    response = llm(
        prompt,
        max_tokens=128,
        temperature=0.7,
        stop=["system", "user", "assistant", "###"],
        echo=False
    )
    
    return response['choices'][0]['text'].strip()

# Example
question = "Show all active users"
sql = generate_sql(question)
print(sql)
# Output: SELECT * FROM users WHERE status = 'active'

Downloads last month: -

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants