[Doc] Add the use of the tables in the QuickStart
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README.md
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@@ -52,9 +52,9 @@ For now, the standalone decoder is open-sourced and fully functional without hav
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This model is static, trained on an offline dataset. Future versions may be released to enhance its performance on specialized tasks.
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**
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> Note that you need `transformers>=4.37.0` to use `TableGPT2`:
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> ```sh
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "tablegpt/TableGPT2-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):]
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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@@ -121,6 +150,7 @@ For deployment, we recommend using vLLM.
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}'
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```
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**License**
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This model is static, trained on an offline dataset. Future versions may be released to enhance its performance on specialized tasks.
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**QuickStart**
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This code snippet demonstrates how to build a prompt with table information, and shows how to load the tokenizer, load the model, and generate content.
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> Note that you need `transformers>=4.37.0` to use `TableGPT2`:
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> ```sh
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Using pandas to read some structured data
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import pandas as pd
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from io import StringIO
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# single table
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EXAMPLE_CSV_CONTENT = """
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"Loss","Date","Score","Opponent","Record","Attendance"
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"Hampton (14β12)","September 25","8β7","Padres","67β84","31,193"
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"Speier (5β3)","September 26","3β1","Padres","67β85","30,711"
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"Elarton (4β9)","September 22","3β1","@ Expos","65β83","9,707"
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"Lundquist (0β1)","September 24","15β11","Padres","67β83","30,774"
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"Hampton (13β11)","September 6","9β5","Dodgers","61β78","31,407"
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"""
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csv_file = StringIO(EXAMPLE_CSV_CONTENT)
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df = pd.read_csv(csv_file)
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# Some data preprocessing
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# code
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model_name = "tablegpt/TableGPT2-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name, torch_dtype="auto", device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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example_prompt_template = """Given access to several pandas dataframes, write the Python code to answer the user's question.
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/*
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"{var_name}.head(5).to_string(index=False)" as follows:
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{df_info}
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*/
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Question: {user_question}
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"""
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question = "εͺδΊζ―θ΅ηζη»©θΎΎε°δΊ40θ40θ΄οΌ"
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prompt = example_prompt_template.format(
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var_name="df",
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df_info=df.head(5).to_string(index=False),
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user_question=question,
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)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt},
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(**model_inputs, max_new_tokens=512)
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generated_ids = [
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output_ids[len(input_ids) :]
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for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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}'
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```
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For more details about how to use TableGPT2, please refer to [our repository on GitHub](https://github.com/tablegpt/tablegpt-agent)
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**License**
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