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

bc78c72

verified ·

1 Parent(s): 4623ea9

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ 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  |
 | CodeLlama (13B)      |  43.4  |  47.2  |  57.2  |  49.7   |   38.3  |  21.9  |     47.6     |   43.6  |
@@ -33,17 +33,17 @@ The prompts we used for generating code solutions and text answers are introduce
 ### Code Solution
 The prompt template for the insert, delete, update, query, and plot operations on a single table.
 ```
-Below are the first few lines of a CSV file. You need to write a Python program to solve the provided question.
 Header and first few lines of CSV file:
 {csv_data}
-Question: {question}
 ```
 The prompt template for the merge operation on two tables.
 ```
-Below are the first few lines two CSV file. You need to write a Python program to solve the provided question.
 Header and first few lines of CSV file 1:
 {csv_data1}
@@ -51,7 +51,7 @@ Header and first few lines of CSV file 1:
 Header and first few lines of CSV file 2:
 {csv_data2}
-Question: {question}
 ```
 The csv_data field is filled with the first few lines of your provided table file. Below is an example:
@@ -67,7 +67,7 @@ I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
 ### Text Answer
 The prompt template for direct text answer generation on short tables.
 ````
-Offer a thorough and accurate solution that directly addresses the Question outlined in the [Question].
 ### [Table Text]
 {table_descriptions}
@@ -79,5 +79,5 @@ Offer a thorough and accurate solution that directly addresses the Question outl
 ### [Question]
 {question}
-### [Solution]
 ````

 | 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  |
 | CodeLlama (13B)      |  43.4  |  47.2  |  57.2  |  49.7   |   38.3  |  21.9  |     47.6     |   43.6  |
 ### Code Solution
 The prompt template for the insert, delete, update, query, and plot operations on a single table.
 ```
+[INST]Below are the first few lines of a CSV file. You need to write a Python program to solve the provided question.
 Header and first few lines of CSV file:
 {csv_data}
+Question: {question}[/INST]
 ```
 The prompt template for the merge operation on two tables.
 ```
+[INST]Below are the first few lines two CSV file. You need to write a Python program to solve the provided question.
 Header and first few lines of CSV file 1:
 {csv_data1}
 Header and first few lines of CSV file 2:
 {csv_data2}
+Question: {question}[/INST]
 ```
 The csv_data field is filled with the first few lines of your provided table file. Below is an example:
 ### Text Answer
 The prompt template for direct text answer generation on short tables.
 ````
+[INST]Offer a thorough and accurate solution that directly addresses the Question outlined in the [Question].
 ### [Table Text]
 {table_descriptions}
 ### [Question]
 {question}
+### [Solution][INST/]
 ````