Summarization
Transformers
Safetensors
English
phi
text-generation
arxiv
custom_code
text-generation-inference
Instructions to use AlgorithmicResearchGroup/phi-metamath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlgorithmicResearchGroup/phi-metamath with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="AlgorithmicResearchGroup/phi-metamath", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AlgorithmicResearchGroup/phi-metamath", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("AlgorithmicResearchGroup/phi-metamath", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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# TL;DR
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This is a Phi-1_5 model trained on [
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## Model Description
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## Training Data
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The model was trained on [
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## Training procedure
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# TL;DR
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This is a Phi-1_5 model trained on [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA). This model is for research purposes only and ***should not be used in production settings***.
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## Model Description
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## Training Data
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The model was trained on [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), a dataset of question/answer pairs.
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## Training procedure
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