Instructions to use mradermacher/Think2SQL-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Think2SQL-14B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Think2SQL-14B-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/Think2SQL-14B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/Think2SQL-14B-GGUF",
	filename="Think2SQL-14B.IQ4_XS.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 mradermacher/Think2SQL-14B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/Think2SQL-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/Think2SQL-14B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/Think2SQL-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/Think2SQL-14B-GGUF:Q4_K_M

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 mradermacher/Think2SQL-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Think2SQL-14B-GGUF:Q4_K_M

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 mradermacher/Think2SQL-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Think2SQL-14B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Think2SQL-14B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Think2SQL-14B-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Think2SQL-14B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mradermacher/Think2SQL-14B-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 mradermacher/Think2SQL-14B-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 mradermacher/Think2SQL-14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Think2SQL-14B-GGUF to start chatting

Docker Model Runner
How to use mradermacher/Think2SQL-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Think2SQL-14B-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Think2SQL-14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Think2SQL-14B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Think2SQL-14B-GGUF-Q4_K_M

List all available models

lemonade list

About

static quants of https://huggingface.co/anonymous-2321/Think2SQL-14B

For a convenient overview and download list, visit our model page for this model.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	5.9
GGUF	Q3_K_S	6.8
GGUF	Q3_K_M	7.4	lower quality
GGUF	Q3_K_L	8.0
GGUF	IQ4_XS	8.3
GGUF	Q4_K_S	8.7	fast, recommended
GGUF	Q4_K_M	9.1	fast, recommended
GGUF	Q5_K_S	10.4
GGUF	Q5_K_M	10.6
GGUF	Q6_K	12.2	very good quality
GGUF	Q8_0	15.8	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Downloads last month: 26

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for mradermacher/Think2SQL-14B-GGUF

Base model

anonymous-2321/Think2SQL-14B

Quantized

(1)

this model