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

Libraries

How to use duyntnet/TableLLM-13b-imatrix-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="duyntnet/TableLLM-13b-imatrix-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/TableLLM-13b-imatrix-GGUF", dtype="auto")

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="duyntnet/TableLLM-13b-imatrix-GGUF",
	filename="TableLLM-13b-IQ1_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 duyntnet/TableLLM-13b-imatrix-GGUF with llama.cpp:

Install from brew

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

How to use duyntnet/TableLLM-13b-imatrix-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "duyntnet/TableLLM-13b-imatrix-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/TableLLM-13b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "duyntnet/TableLLM-13b-imatrix-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/TableLLM-13b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Unsloth Studio new

How to use duyntnet/TableLLM-13b-imatrix-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 duyntnet/TableLLM-13b-imatrix-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 duyntnet/TableLLM-13b-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Quantizations of https://huggingface.co/RUCKBReasoning/TableLLM-13b

Inference Clients/UIs

From original readme

Prompt Template

The prompts we used for generating code solutions and text answers are introduced below.

Code Solution

The prompt template for the insert, delete, update, query, and plot operations on a single table.

[INST]Below are the first few lines of a CSV file. You need to write a Python program to solve the provided question.

Header and first few lines of CSV file:
{csv_data}

Question: {question}[/INST]

The prompt template for the merge operation on two tables.

[INST]Below are the first few lines two CSV file. You need to write a Python program to solve the provided question.

Header and first few lines of CSV file 1:
{csv_data1}

Header and first few lines of CSV file 2:
{csv_data2}

Question: {question}[/INST]

The csv_data field is filled with the first few lines of your provided table file. Below is an example:

Sex,Length,Diameter,Height,Whole weight,Shucked weight,Viscera weight,Shell weight,Rings
M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
M,0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10
I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7

Text Answer

The prompt template for direct text answer generation on short tables.

[INST]Offer a thorough and accurate solution that directly addresses the Question outlined in the [Question].
### [Table Text]
{table_descriptions}

### [Table]
```
{table_in_csv}
```

### [Question]
{question}

### [Solution][INST/]

Downloads last month: 552

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit