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

a13cc29

verified ·

1 Parent(s): 1816e23

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@@ -15,7 +15,7 @@ We evaluate the code solution generation ability of TLLM on three benchmarks: Wi
 | :------------------- | :----: | :----: | :----: | :-----: | :-----: | :----: | :----------: | :-----: |
 | TaPEX                |  38.5  |    –   |    –   |    –    |   83.9  |  15.0  |       /      |   45.8  |
 | TaPas                |  31.5  |    –   |    –   |    –    |   74.2  |  23.1  |       /      |   42.92 |
-| TableLlama           |  24.0  |  22.2  |  18.9  |   6.4   |   43.7  |   9.0  |       /      |   20.7  |
 | GPT3.5               |  58.5  |<ins>72.1</ins>|  71.2  |  60.8   |   81.7   |  67.4  | 77.1 |   69.8  |
 | GPT4                 |**74.1**|**77.1**|**78.4**|**69.5** |   84.0  |  69.5  |     77.8     | **75.8**|
 | Llama2-Chat (13B)    |  48.8  |  49.6  |  67.7  |  61.5   |    –    |    –   |       –      |   56.9  |

 | :------------------- | :----: | :----: | :----: | :-----: | :-----: | :----: | :----------: | :-----: |
 | TaPEX                |  38.5  |    –   |    –   |    –    |   83.9  |  15.0  |       /      |   45.8  |
 | TaPas                |  31.5  |    –   |    –   |    –    |   74.2  |  23.1  |       /      |   42.92 |
+| TableLlama           |  24.0  |  22.2  |  20.5  |   6.4   |   43.7  |   9.0  |       /      |   20.7  |
 | GPT3.5               |  58.5  |<ins>72.1</ins>|  71.2  |  60.8   |   81.7   |  67.4  | 77.1 |   69.8  |
 | GPT4                 |**74.1**|**77.1**|**78.4**|**69.5** |   84.0  |  69.5  |     77.8     | **75.8**|
 | Llama2-Chat (13B)    |  48.8  |  49.6  |  67.7  |  61.5   |    –    |    –   |       –      |   56.9  |