Instructions to use RUCKBReasoning/TableLLM-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Add library_name and pipeline_tag
Browse filesThis PR improves the model card by adding the `transformers` library as well as the `table-question-answering` pipeline tag,
making sure people can find the model at https://huggingface.co/models?pipeline_tag=table-question-answering.
README.md
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---
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license: llama2
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datasets:
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- RUCKBReasoning/TableLLM-SFT
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language:
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- en
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tags:
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- Table
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- QA
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- Code
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---
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# TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
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| **[Paper](https://arxiv.org/abs/2403.19318)** | **[Training set](https://huggingface.co/datasets/RUCKBReasoning/TableLLM-SFT)** | **[Github](https://github.com/
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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-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) and [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf).
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### [Solution][INST/]
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````
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For more details about how to use TableLLM, please refer to our GitHub page: <https://github.com/TableLLM/TableLLM>
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---
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datasets:
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- RUCKBReasoning/TableLLM-SFT
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language:
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- en
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license: llama2
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tags:
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- Table
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- QA
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- Code
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pipeline_tag: table-question-answering
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library_name: transformers
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---
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# TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
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| **[Paper](https://arxiv.org/abs/2403.19318)** | **[Training set](https://huggingface.co/datasets/RUCKBReasoning/TableLLM-SFT)** | **[Github](https://github.com/TableLLM/TableLLM)** | **[Homepage](https://tablellm.github.io/)** |
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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-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) and [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf).
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### [Solution][INST/]
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````
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For more details about how to use TableLLM, please refer to our GitHub page: <https://github.com/TableLLM/TableLLM>
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# File information
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The repository contains the following file information:
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Filename: special_tokens_map.json
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Content: {
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "[PAD]",
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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Filename: model.safetensors.index.json
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Content: {
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"metadata": {
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"total_size": 26032056320
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