Instructions to use bartowski/TableLLM-13b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/TableLLM-13b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/TableLLM-13b-GGUF",
	filename="TableLLM-13b-IQ3_M.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 bartowski/TableLLM-13b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/TableLLM-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/TableLLM-13b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/TableLLM-13b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/TableLLM-13b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/TableLLM-13b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M

Ollama
How to use bartowski/TableLLM-13b-GGUF with Ollama:
```
ollama run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/TableLLM-13b-GGUF to start chatting

Docker Model Runner
How to use bartowski/TableLLM-13b-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/TableLLM-13b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/TableLLM-13b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TableLLM-13b-GGUF-Q4_K_M

List all available models

lemonade list

Llamacpp Quantizations of TableLLM-13b

Using llama.cpp release b2589 for quantization.

Original model: https://huggingface.co/RUCKBReasoning/TableLLM-13b

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Description
TableLLM-13b-Q8_0.gguf	Q8_0	13.83GB	Extremely high quality, generally unneeded but max available quant.
TableLLM-13b-Q6_K.gguf	Q6_K	10.67GB	Very high quality, near perfect, recommended.
TableLLM-13b-Q5_K_M.gguf	Q5_K_M	9.23GB	High quality, very usable.
TableLLM-13b-Q5_K_S.gguf	Q5_K_S	8.97GB	High quality, very usable.
TableLLM-13b-Q5_0.gguf	Q5_0	8.97GB	High quality, older format, generally not recommended.
TableLLM-13b-Q4_K_M.gguf	Q4_K_M	7.86GB	Good quality, uses about 4.83 bits per weight.
TableLLM-13b-Q4_K_S.gguf	Q4_K_S	7.42GB	Slightly lower quality with small space savings.
TableLLM-13b-IQ4_NL.gguf	IQ4_NL	7.41GB	Decent quality, similar to Q4_K_S, new method of quanting,
TableLLM-13b-IQ4_XS.gguf	IQ4_XS	7.01GB	Decent quality, new method with similar performance to Q4.
TableLLM-13b-Q4_0.gguf	Q4_0	7.36GB	Decent quality, older format, generally not recommended.
TableLLM-13b-Q3_K_L.gguf	Q3_K_L	6.92GB	Lower quality but usable, good for low RAM availability.
TableLLM-13b-Q3_K_M.gguf	Q3_K_M	6.33GB	Even lower quality.
TableLLM-13b-IQ3_M.gguf	IQ3_M	5.98GB	Medium-low quality, new method with decent performance.
TableLLM-13b-IQ3_S.gguf	IQ3_S	5.65GB	Lower quality, new method with decent performance, recommended over Q3 quants.
TableLLM-13b-Q3_K_S.gguf	Q3_K_S	5.65GB	Low quality, not recommended.
TableLLM-13b-Q2_K.gguf	Q2_K	4.85GB	Extremely low quality, not recommended.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

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GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

bartowski
/

TableLLM-13b-GGUF

Llamacpp Quantizations of TableLLM-13b

Dataset used to train bartowski/TableLLM-13b-GGUF