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

Libraries

How to use RUCKBReasoning/TableLLM-13b with Transformers:

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

pipe = pipeline("text-generation", model="RUCKBReasoning/TableLLM-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RUCKBReasoning/TableLLM-13b")
model = AutoModelForCausalLM.from_pretrained("RUCKBReasoning/TableLLM-13b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RUCKBReasoning/TableLLM-13b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RUCKBReasoning/TableLLM-13b

SGLang

How to use RUCKBReasoning/TableLLM-13b 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 "RUCKBReasoning/TableLLM-13b" \
    --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": "RUCKBReasoning/TableLLM-13b",
		"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 "RUCKBReasoning/TableLLM-13b" \
        --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": "RUCKBReasoning/TableLLM-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

KAKA22 commited on May 14, 2024

Commit

c992016

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1 Parent(s): 9deab59

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@@ -12,7 +12,7 @@ tags:
 # TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
-| **[Paper](https://arxiv.org/abs/2403.19318)** | **[Training set](https://huggingface.co/datasets/RUCKBReasoning/TableLLM-SFT)** | **[Github](https://github.com/RUCKBReasoning/TableLLM)** | **[Homepage](https://tablellm.github.io/)** | **[Platform](http://36.103.203.47:27824/)** |
 We present **TableLLM**, a powerful large language model designed to handle tabular data manipulation tasks efficiently, whether they are embedded in spreadsheets or documents, meeting the demands of real office scenarios. The TableLLM series encompasses two distinct scales: [TableLLM-7B](https://huggingface.co/RUCKBReasoning/TableLLM-7b) and [TableLLM-13B](https://huggingface.co/RUCKBReasoning/TableLLM-13b), which are fine-tuned based on CodeLlama-7B and 13B.

 # TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
+| **[Paper](https://arxiv.org/abs/2403.19318)** | **[Training set](https://huggingface.co/datasets/RUCKBReasoning/TableLLM-SFT)** | **[Github](https://github.com/RUCKBReasoning/TableLLM)** | **[Homepage](https://tablellm.github.io/)** |
 We present **TableLLM**, a powerful large language model designed to handle tabular data manipulation tasks efficiently, whether they are embedded in spreadsheets or documents, meeting the demands of real office scenarios. The TableLLM series encompasses two distinct scales: [TableLLM-7B](https://huggingface.co/RUCKBReasoning/TableLLM-7b) and [TableLLM-13B](https://huggingface.co/RUCKBReasoning/TableLLM-13b), which are fine-tuned based on CodeLlama-7B and 13B.