Instructions to use sriram882004/SQL-Socratic-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sriram882004/SQL-Socratic-Models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sriram882004/SQL-Socratic-Models",
	filename="gemma2/gemma2_9b__fft__base__masked/gguf/gemma2_9b__fft__base__masked_q8_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sriram882004/SQL-Socratic-Models with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sriram882004/SQL-Socratic-Models:Q8_0
# Run inference directly in the terminal:
llama-cli -hf sriram882004/SQL-Socratic-Models:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sriram882004/SQL-Socratic-Models:Q8_0
# Run inference directly in the terminal:
llama-cli -hf sriram882004/SQL-Socratic-Models: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 sriram882004/SQL-Socratic-Models:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf sriram882004/SQL-Socratic-Models: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 sriram882004/SQL-Socratic-Models:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sriram882004/SQL-Socratic-Models:Q8_0

Use Docker

docker model run hf.co/sriram882004/SQL-Socratic-Models:Q8_0

LM Studio
Jan
Ollama
How to use sriram882004/SQL-Socratic-Models with Ollama:
```
ollama run hf.co/sriram882004/SQL-Socratic-Models:Q8_0
```

Unsloth Studio new

How to use sriram882004/SQL-Socratic-Models 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 sriram882004/SQL-Socratic-Models 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 sriram882004/SQL-Socratic-Models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sriram882004/SQL-Socratic-Models to start chatting

Docker Model Runner
How to use sriram882004/SQL-Socratic-Models with Docker Model Runner:
```
docker model run hf.co/sriram882004/SQL-Socratic-Models:Q8_0
```

Lemonade

How to use sriram882004/SQL-Socratic-Models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sriram882004/SQL-Socratic-Models:Q8_0

Run and chat with the model

lemonade run user.SQL-Socratic-Models-Q8_0

List all available models

lemonade list

sriram882004 commited on 10 days ago

Commit

145d1cd

verified ·

1 Parent(s): 0cdbc0f

Create README.md

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README.md +26 -0

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	@@ -0,0 +1,26 @@

+---
+license: mit
+---
+# SQL Socratic Models
+This repository contains fine-tuned LLMs for Text-to-SQL using Socratic prompting and reasoning decomposition.
+## Models
+- phi3_rq4
+- qwen25
+- llama31
+## Method
+We fine-tune models on SQL generation tasks with Socratic reasoning prompts.
+## Contributions
+- Fine-tuning across multiple architectures
+- Ablation studies on reasoning components
+- Evaluation on Text-to-SQL benchmarks
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("sriram882004/SQL-Socratic-Models/phi3_rq4")
+tokenizer = AutoTokenizer.from_pretrained(...)