Instructions to use imahwashere/TexttoSQLish_Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imahwashere/TexttoSQLish_Code with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("imahwashere/TexttoSQLish_Code", dtype="auto")

llama-cpp-python

How to use imahwashere/TexttoSQLish_Code with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="imahwashere/TexttoSQLish_Code",
	filename="unsloth.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use imahwashere/TexttoSQLish_Code with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
llama-cli -hf imahwashere/TexttoSQLish_Code:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
llama-cli -hf imahwashere/TexttoSQLish_Code: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 imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf imahwashere/TexttoSQLish_Code: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 imahwashere/TexttoSQLish_Code:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf imahwashere/TexttoSQLish_Code:Q8_0

Use Docker

docker model run hf.co/imahwashere/TexttoSQLish_Code:Q8_0

LM Studio
Jan
Ollama
How to use imahwashere/TexttoSQLish_Code with Ollama:
```
ollama run hf.co/imahwashere/TexttoSQLish_Code:Q8_0
```

Unsloth Studio

How to use imahwashere/TexttoSQLish_Code 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 imahwashere/TexttoSQLish_Code 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 imahwashere/TexttoSQLish_Code to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for imahwashere/TexttoSQLish_Code to start chatting

How to use imahwashere/TexttoSQLish_Code with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0

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": "imahwashere/TexttoSQLish_Code:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use imahwashere/TexttoSQLish_Code with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf imahwashere/TexttoSQLish_Code:Q8_0

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 imahwashere/TexttoSQLish_Code:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use imahwashere/TexttoSQLish_Code with Docker Model Runner:
```
docker model run hf.co/imahwashere/TexttoSQLish_Code:Q8_0
```

Lemonade

How to use imahwashere/TexttoSQLish_Code with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull imahwashere/TexttoSQLish_Code:Q8_0

Run and chat with the model

lemonade run user.TexttoSQLish_Code-Q8_0

List all available models

lemonade list

imahwashere commited on Aug 28, 2025

Commit

e24e9ff

verified ·

1 Parent(s): f816dcc

Update README.md

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README.md +25 -1

README.md CHANGED Viewed

@@ -9,8 +9,32 @@ tags:
 license: apache-2.0
 language:
 - en
 ---
 # Uploaded  model
 - **Developed by:** imahwashere
@@ -19,4 +43,4 @@ language:
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 license: apache-2.0
 language:
 - en
+datasets:
+- gretelai/synthetic_text_to_sql
 ---
+### Text-to-SQL Model
+A fine-tuned Llama 3.2 3B model specialized for converting natural language queries into SQL statements. This model transforms everyday questions into precise database queries, making data accessible to everyone.
+### Desired Use Cases
+Perfect for applications requiring natural language to SQL conversion:
+Business Intelligence: "Show me sales by region for last quarter"
+Data Analytics: "Find customers who haven't purchased in 6 months"
+Reporting: "What are the top 10 products by revenue?"
+Database Querying: Making databases accessible to non-technical users
+### Training Data
+Trained on the gretelai/synthetic_text_to_sql dataset, which provides:
+High-quality synthetic text-to-SQL pairs
+Diverse query patterns and complexity levels
+Multiple database schemas and domains
+Robust coverage of SQL operations and functions
+### Model Performance
+IDK MAN, it does what it does brev.
 # Uploaded  model
 - **Developed by:** imahwashere
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)