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")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Feb 7, 2024

Commit

d9824fb

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1 Parent(s): cd84e92

Update README.md

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README.md +4 -4

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@@ -4,12 +4,12 @@ license: llama2
 # TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
-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 TLLM series encompasses two distinct scales: [TableLLM-7B](https://huggingface.co/KAKA22/TableLLM-7b) and [TableLLM-13B](https://huggingface.co/KAKA22/TableLLM-13b), which are fine-tuned based on CodeLlama-7B and 13B.
-TLLM generates either a code solution or a direct text answer to handle tabular data manipulation tasks based on different scenarios. Code generation is used for handling spreadsheet-embedded tabular data, which often involves the insert, delete, update, query, merge, and plot operations of tables. Text generation is used for handling document-embedded tabular data, which often involves the query operation of short tables.
 ## Evaluation Results
-We evaluate the code solution generation ability of TLLM on three benchmarks: WikiSQL, Spider and Self-created table operation benchmark. The text answer generation ability is tested on four benchmarks: WikiTableQuestion (WikiTQ), TAT-QA, FeTaQA and OTTQA. The evaluation result is shown below:
 | Model                | WikiTQ | TAT-QA | FeTaQA |  OTTQA  | WikiSQL | Spider | Self-created | Average |
 | :------------------- | :----: | :----: | :----: | :-----: | :-----: | :----: | :----------: | :-----: |
@@ -82,4 +82,4 @@ The prompt template for direct text answer generation on short tables.
 ### [Solution][INST/]
 ````
-For more details about how to use TLLM, please refer to our GitHub page: <https://github.com/TableLLM/TableLLM>

 # TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
+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/KAKA22/TableLLM-7b) and [TableLLM-13B](https://huggingface.co/KAKA22/TableLLM-13b), which are fine-tuned based on CodeLlama-7B and 13B.
+TableLLM generates either a code solution or a direct text answer to handle tabular data manipulation tasks based on different scenarios. Code generation is used for handling spreadsheet-embedded tabular data, which often involves the insert, delete, update, query, merge, and plot operations of tables. Text generation is used for handling document-embedded tabular data, which often involves the query operation of short tables.
 ## Evaluation Results
+We evaluate the code solution generation ability of TableLLM on three benchmarks: WikiSQL, Spider and Self-created table operation benchmark. The text answer generation ability is tested on four benchmarks: WikiTableQuestion (WikiTQ), TAT-QA, FeTaQA and OTTQA. The evaluation result is shown below:
 | Model                | WikiTQ | TAT-QA | FeTaQA |  OTTQA  | WikiSQL | Spider | Self-created | Average |
 | :------------------- | :----: | :----: | :----: | :-----: | :-----: | :----: | :----------: | :-----: |
 ### [Solution][INST/]
 ````
+For more details about how to use TableLLM, please refer to our GitHub page: <https://github.com/TableLLM/TableLLM>