Instructions to use osunlp/TableLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osunlp/TableLlama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="osunlp/TableLlama")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("osunlp/TableLlama") model = AutoModelForCausalLM.from_pretrained("osunlp/TableLlama", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use osunlp/TableLlama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "osunlp/TableLlama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/TableLlama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/osunlp/TableLlama
- SGLang
How to use osunlp/TableLlama 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 "osunlp/TableLlama" \ --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": "osunlp/TableLlama", "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 "osunlp/TableLlama" \ --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": "osunlp/TableLlama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use osunlp/TableLlama with Docker Model Runner:
docker model run hf.co/osunlp/TableLlama
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README.md
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[TableLlama-7B](https://huggingface.co/osunlp/TableLlama/)
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##
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The models are trained on the 🤗 [TableInstruct Dataset](https://huggingface.co/datasets/osunlp/TableInstruct), which includes a comprehensive table-based instruction tuning dataset that covers a variety of real-world tables and realistic tasks. We include 14 datasets of 11 tasks in total. Check out the dataset card for more details.
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```
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## Limitations
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We've tried our best to build table generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the table tasks and datasets. Still not all
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## Citation
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[TableLlama-7B](https://huggingface.co/osunlp/TableLlama/)
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## Data
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The models are trained on the 🤗 [TableInstruct Dataset](https://huggingface.co/datasets/osunlp/TableInstruct), which includes a comprehensive table-based instruction tuning dataset that covers a variety of real-world tables and realistic tasks. We include 14 datasets of 11 tasks in total. Check out the dataset card for more details.
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```
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## Limitations
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We've tried our best to build table generalist models. However, we acknowledge that the models' performance may vary based on the complexity and specifics of the table tasks and datasets. Still not all table-based tasks can be covered comprehensively.
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## Citation
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