YAML Metadata
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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Model Details
Model Description
- Developed by: Timofej Kiselev (tfshaman)
- Model type: Mistral finetuned for solving MWPs using symbolic expressions with SymPy
- Language(s) (NLP): English, Python with SymPy
- License: Apache-2.0
- Finetuned from model [optional]: meta-math/MetaMath-Mistral-7B
- Trained on: Research Center for Informatics | CTU Prague, RCI cluster
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: https://dspace.cvut.cz/bitstream/handle/10467/115466/F3-BP-2024-Kiselev-Timofej-Thesis_Timofej_Kiselev.pdf
Uses
Input format: f"Question {your_math_word_problem}\n\nAnswer: "
Direct Use
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
config = PeftConfig.from_pretrained("tfshaman/SymPy-Mistral")
base_model = AutoModelForCausalLM.from_pretrained("meta-math/MetaMath-Mistral-7B", quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained("tfshaman/SymPy-Mistral-tokenizer", use_fast=False, padding_side="left")
base_model.resize_token_embeddings(len(tokenizer))
tokenizer.pad_token = "<s>"
tokenizer.padding_side='left'
model = PeftModel.from_pretrained(base_model, "tfshaman/SymPy-Mistral", quantization_config=bnb_config)
model = model.to("cuda")
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Citation
@mastersthesis{timofej2024velke, title={Velk{'e} jazykov{'e} modely pro numerick{'e} dotazy}, author={Timofej, Kiselev}, type={{B.S.} thesis}, year={2024}, school={{\v{C}}esk{'e} vysok{'e} u{\v{c}}en{'\i} technick{'e} v Praze. Vypo{\v{c}}etn{'\i} a informa{\v{c}}n{'\i} centrum.} }
Framework versions
- PEFT 0.7.1
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- 3
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Base model
meta-math/MetaMath-Mistral-7B