77 lines
2.4 KiB
Markdown
77 lines
2.4 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- nllg/TikZilla-3B
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pipeline_tag: text-generation
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tags:
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- tikz
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- latex
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- code-generation
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- scientific-figures
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---
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# Model Card for TikZilla-3B-RL
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TikZilla-3B-RL is a language model for generating TikZ/LaTeX figures from natural language descriptions.
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It is based on **TikZilla-3B** and was trained with **reinforcement learning (RL)** on **DaTikZ-V4** for scientific figure generation.
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## Installation
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```bash
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pip install torch==2.5.1 transformers==4.53.2 accelerate==1.8.1
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```
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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model_id = "nllg/TikZilla-3B-RL"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
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pad_token_id = tokenizer.pad_token_id or tokenizer.eos_token_id or eos_token_id
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gen_config = GenerationConfig(
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do_sample=True,
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temperature=1.0,
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top_p=0.9,
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max_new_tokens=2048,
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eos_token_id=eos_token_id,
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pad_token_id=pad_token_id,
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)
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your_input_description = "A scientific line plot showing two curves. The x-axis is labeled 'Time' ranging from 0 to 100, and the y-axis is labeled 'Value' ranging from 0 to 1. The first curve is a blue solid line that gradually increases from near 0 and levels off around 0.9. The second curve is a red dashed line that rises to a peak around the middle of the plot and then decreases. A legend in the upper right labels the blue line as 'Model A' and the red dashed line as 'Model B'. The background is white with light gray grid lines."
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messages = [
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{
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"role": "user",
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"content": (
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"Generate a complete LaTeX document that contains a TikZ figure according to the following requirements:\n"
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+ your_input_description +
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"\nWrap your code using \\documentclass[tikz]{standalone}, and include \\begin{document}...\\end{document}. "
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"Only output valid LaTeX code with no extra text."
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),
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}
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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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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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output_ids = model.generate(**inputs, generation_config=gen_config)
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response_ids = output_ids[0][len(inputs["input_ids"][0]):]
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output = tokenizer.decode(response_ids, skip_special_tokens=True)
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print(output)
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``` |