Instructions to use ragul6606/text2sql-qwen2.5-coder-1.5b-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 ragul6606/text2sql-qwen2.5-coder-1.5b-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 ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use ragul6606/text2sql-qwen2.5-coder-1.5b-gguf with Ollama:
```
ollama run hf.co/ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M
```
Unsloth Desktop

How to use ragul6606/text2sql-qwen2.5-coder-1.5b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ragul6606/text2sql-qwen2.5-coder-1.5b-gguf with Docker Model Runner:
```
docker model run hf.co/ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M
```

Lemonade

How to use ragul6606/text2sql-qwen2.5-coder-1.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.text2sql-qwen2.5-coder-1.5b-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ragul6606/text2sql-qwen2.5-coder-1.5b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

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 ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ragul6606/text2sql-qwen2.5-coder-1.5b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ragul6606/text2sql-qwen2.5-coder-1.5b-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

text2sql-qwen2.5-coder-1.5b-gguf

Fine-tuned (QLoRA) text-to-SQL model, quantized to Q4_K_M GGUF for local use.

Prompt format (important)

System: You are a text-to-SQL assistant. Given a database schema and a question, reply with one valid SQL query and nothing else.

User: Schema: CREATE TABLE employees (id INTEGER, name TEXT, salary REAL)

Question: Who are the 3 highest paid employees?

Use temperature 0.

Results (held-out schemas)

model	exact_match	valid_sql
base	X	X
fine-tuned	X	X

Limitations

Trained mainly on simple, single-table queries (WikiSQL / Spider style).
Not evaluated by execution on real databases.
Review generated SQL before running it, and use a read-only connection.

Credits

Training data: b-mc2/sql-create-context (CC-BY-4.0). Base model: Qwen2.5-Coder-1.5B-Instruct.

Downloads last month: 87

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for ragul6606/text2sql-qwen2.5-coder-1.5b-gguf

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Quantized

(199)

this model

ragul6606
/

text2sql-qwen2.5-coder-1.5b-gguf

text2sql-qwen2.5-coder-1.5b-gguf

Prompt format (important)

Results (held-out schemas)

Limitations

Credits

Model tree for ragul6606/text2sql-qwen2.5-coder-1.5b-gguf

Dataset used to train ragul6606/text2sql-qwen2.5-coder-1.5b-gguf