Instructions to use Ashoksainibadu/fine-tuned-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Ashoksainibadu/fine-tuned-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 Ashoksainibadu/fine-tuned-text2sql:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ashoksainibadu/fine-tuned-text2sql:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ashoksainibadu/fine-tuned-text2sql:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ashoksainibadu/fine-tuned-text2sql: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 Ashoksainibadu/fine-tuned-text2sql:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Ashoksainibadu/fine-tuned-text2sql:Q4_K_M

Use Docker

docker model run hf.co/Ashoksainibadu/fine-tuned-text2sql:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use Ashoksainibadu/fine-tuned-text2sql with Ollama:
```
ollama run hf.co/Ashoksainibadu/fine-tuned-text2sql:Q4_K_M
```
Unsloth Desktop

How to use Ashoksainibadu/fine-tuned-text2sql with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ashoksainibadu/fine-tuned-text2sql: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": "Ashoksainibadu/fine-tuned-text2sql:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Ashoksainibadu/fine-tuned-text2sql with Docker Model Runner:
```
docker model run hf.co/Ashoksainibadu/fine-tuned-text2sql:Q4_K_M
```

Lemonade

How to use Ashoksainibadu/fine-tuned-text2sql with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ashoksainibadu/fine-tuned-text2sql:Q4_K_M

Run and chat with the model

lemonade run user.fine-tuned-text2sql-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Ashoksainibadu/fine-tuned-text2sql with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ashoksainibadu/fine-tuned-text2sql: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 Ashoksainibadu/fine-tuned-text2sql:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Ashoksainibadu/fine-tuned-text2sql with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ashoksainibadu/fine-tuned-text2sql: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 "Ashoksainibadu/fine-tuned-text2sql: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"

Model Card: Fine-Tuned Text-to-SQL Assistant (QLoRA)

This repository contains the fine-tuned LoRA adapter and serving configuration for Text-to-SQL Assistant, adapted from Qwen/Qwen2.5-Coder-3B-Instruct on Spider benchmark training pairs and guarded with AST SQL safety guardrails and a read-only SQLite execution sandbox.

1. Model Details

Base Model: Qwen/Qwen2.5-Coder-3B-Instruct (Hangzhou Tongyi Laboratory Technology Co., Ltd. / Alibaba Cloud)
Adapter Architecture: QLoRA ($r=16$, $\alpha=32$, target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
Serving Quantization: GGUF Q4_K_M (1.9 GB footprint, cut from 6.2 GB FP16)
Serving Path: Safe SQLite execution sandbox with sqlglot AST validation
Demo Deployment & Accounting: Non-commercial research demo serving Chinook SQLite (chinook_1). 13 questions attempted: 10 passing presets in UI, 3 adversarial queries blocked by guardrail (recorded in results/demo_examples.json).
Phase 3 Ablation Status: Kaggle base model ablation is marked UNVERIFIED, harness suspect (see docs/LIMITATIONS.md).

2. Licensing, Attribution & Usage Terms (CRITICAL)

Base Model License: Qwen Research License Agreement

The base model Qwen/Qwen2.5-Coder-3B-Instruct is governed by the Qwen Research License Agreement (Release Date: September 19, 2024), NOT Apache-2.0.

Permitted Purpose: Non-commercial research and evaluation purposes only.
Commercial Use: Strict prohibition on commercial use without obtaining a separate commercial license from Alibaba Cloud (model-business@notice.qwencloud.com).
Derivative Distribution: Any redistribution of fine-tuned adapters, merged checkpoints, or quantized weights must retain the Qwen Research License copy, include prominent modification notices, and provide the attribution:

"Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved." "Built with Qwen"
Notice on Commercial Alternatives: For unrestricted commercial applications, users should retrain or serve an Apache-2.0 licensed model from the Qwen2.5-Coder family (e.g. 1.5B, 7B, 14B, or 32B).

Other Third-Party Licenses

Training Dataset (Spider): Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
Demo Database (Chinook): MIT License (Copyright (c) 2008-2024 Luis Rocha).
SQL Parser (sqlglot): MIT License (Copyright (c) Toby Mao).

3. Empirical Accuracy & Latency Benchmarks

All metrics below are derived directly from reproducible runs recorded in results/eval_spider_latest.json and results/latency_latest.json (Rule R1):

Model Configuration	Format / Size	Execution Accuracy (Spider Dev, n=1,034)	95% Wilson / Bootstrap CI	Latency p50	Latency p95	Serving Hardware
Base Zero-Shot	FP16 (6.2 GB)	62.28% (644 / 1,034)	[59.38%, 65.18%]	2.16 s	5.82 s	Linux GPU (Cloud)
Base 3-Shot	FP16 (6.2 GB)	66.15% (684 / 1,034)	[63.25%, 68.96%]	2.23 s	6.01 s	Linux GPU (Cloud)
Fine-Tuned Merged	FP16 (6.2 GB)	71.57% (740 / 1,034)	[68.86%, 74.27%]	2.19 s	5.91 s	Linux GPU (Cloud)
Fine-Tuned Quantized	GGUF Q4_K_M (1.9 GB)	70.70% (731 / 1,034)	[67.89%, 73.40%]	4.68 s	11.23 s	Local CPU (4 cores, 31.4 GB RAM)
Fine-Tuned + Guardrail	GGUF Q4_K_M (1.9 GB)	70.50% (729 / 1,034)	[67.69%, 73.21%]	4.71 s	11.31 s	Local CPU (4 cores, 31.4 GB RAM)

4. Security & Safety Evaluation

Evaluated against multi-path adversarial red-team suites (results/redteam_latest.json, results/METRICS.md):

Path A Static AST Attack Block Rate: 87.50% (105 / 120) raw attacks blocked statically; 96.33% (105 / 109) adjusted for audited harmless queries.
Path C Read-Only Sandbox Defense Rate: 100.00% (120 / 120) of attack payloads intercepted or neutralized at database layer.
Path D Full Pipeline Attack Success Rate: 0.00% (0 / 120) across all 120 direct adversarial SQL attacks.
Path B End-to-End Prompt Injection Success: 0.00% (0 / 60) on natural language instruction override attacks.
Spider Dev Legitimate Query False-Positive Rate: 0.19% (2 / 1,034) false blocks on Spider dev; 99.37% (1,103 / 1,110) legitimate query pass rate on combined benchmark.
Policy Escape vs Harm Split (Held-Out V2): Pre-fix policy escape rate was 8.33% (4 / 48), but Harm Rate was 0.00% (0 / 48) because the read-only sandbox authorizer neutralized every escaped payload.
Held-Out Evaluation Caveat: An independent heldout_v3 test suite has not yet been evaluated under the active safety policy freeze.

5. Deployment Instructions

Adapter Loading (PyTorch / Transformers) - [NOT PUBLISHED / TODO]

(Note: Adapter weights have not been uploaded to Hugging Face Model Hub. When published, load via:)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "Qwen/Qwen2.5-Coder-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto")
# model = PeftModel.from_pretrained(base_model, "<HF_USERNAME>/fine-tuned-text2sql-adapter")

Private GGUF Serving

To comply with the Qwen Research License, pre-quantized merged GGUF files should be maintained in a private Hugging Face repository and downloaded at container runtime via a scoped Space secret HF_TOKEN.