yuan-tian
/

chartgpt-llama3

@@ -1,199 +1,143 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
+datasets:
+- yuan-tian/chartgpt-dataset
+language:
+- en
+metrics:
+- rouge
+pipeline_tag: text2text-generation
 ---
+# Model Card for ChartGPT-Llama3
 ## Model Details
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
+This model is used to generate charts from natural language. For more information, please refer to the paper.
+* **Model type:** Language model
+* **Language(s) (NLP)**: English
+* **License**: Apache 2.0
+* **Finetuned from model**: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+* **Research paper**: [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://ieeexplore.ieee.org/document/10443572)
+### Model Input Format
+<details>
+<summary> Click to expand </summary>
+Model input on the Step `x`.
+```
+Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+Your response should follow the following format:
+{Step 1 prompt}
+{Step x-1 prompt}
+{Step x prompt}
+### Instruction:
+{instruction}
+### Input:
+Table Name: {table name}
+Table Header: {column names}
+Table Header Type: {column types}
+Table Data Example:
+{data row 1}
+{data row 2}
+Previous Answer:
+{previous answer}
+### Response:
+```
+And the model should output the answer corresponding to step `x`.
+The step 1-6 prompts are as follows:
+```
+Step 1. Select the columns:
+Step 2. Filter the data:
+Step 3. Add aggregate functions:
+Step 4. Choose chart type:
+Step 5. Select encodings:
+Step 6. Sort the data:
+```
+</details>
 ## How to Get Started with the Model
+### Running the Model on a GPU
+An example of a movie dataset with an instruction "Give me a visual representation of the faculty members by their professional status.".
+The model should give the answers to all steps.
+You can use the code below to test if you can run the model successfully.
+<details>
+<summary> Click to expand </summary>
+```python
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+)
+tokenizer = AutoTokenizer.from_pretrained("yuan-tian/chartgpt-llama3")
+model = AutoModelForCausalLM.from_pretrained("yuan-tian/chartgpt-llama3", device_map="auto")
+input_text = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+Your response should follow the following format:
+Step 1. Select the columns:
+Step 2. Filter the data:
+Step 3. Add aggregate functions:
+Step 4. Choose chart type:
+Step 5. Select encodings:
+Step 6. Sort the data:
+### Instruction:
+Give me a visual representation of the faculty members by their professional status.
+### Input:
+Table Name: Faculty
+Table Header: FacID,Lname,Fname,Rank,Sex,Phone,Room,Building
+Table Header Type: quantitative,nominal,nominal,nominal,nominal,quantitative,nominal,nominal
+Table Data Example:
+1082,Giuliano,Mark,Instructor,M,2424,224,NEB
+1121,Goodrich,Michael,Professor,M,3593,219,NEB
+Previous Answer:
+### Response:"""
+inputs = tokenizer(input_text, return_tensors="pt", padding=True).to("cuda")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens = True))
+```
+</details>
 ## Training Details
 ### Training Data
+This model is Fine-tuned from [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the [chartgpt-dataset](https://huggingface.co/datasets/yuan-tian/chartgpt-dataset).
+### Training Procedure
+Plan to update the preprocessing and training procedure in the future.
+## Citation
 **BibTeX:**
+```
+@article{tian2024chartgpt,
+  title={ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language},
+  author={Tian, Yuan and Cui, Weiwei and Deng, Dazhen and Yi, Xinjing and Yang, Yurun and Zhang, Haidong and Wu, Yingcai},
+  journal={IEEE Transactions on Visualization and Computer Graphics},
+  year={2024},
+  pages={1-15},
+  doi={10.1109/TVCG.2024.3368621}
+}
+```