library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags: []
Model Card for Model ID
This is the Sky-T1-32B-Preview model, as described in LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!. The code is available at https://github.com/NovaSky-AI/SkyThought.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: NovaSky AI
- Funded by [optional]: Berkeley Sky Computing Lab, Lambda Labs, Anyscale, and Databricks
- Shared by [optional]: NovaSky AI
- Model type: Qwen2ForCausalLM
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model [optional]: Qwen2
Model Sources [optional]
- Repository: https://github.com/NovaSky-AI/SkyThought
- Paper [optional]: LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
- Demo [optional]: [More Information Needed]
Uses
Direct Use
This model is intended for research purposes, specifically for exploring long chain-of-thought reasoning in large language models. It can be used for math and coding benchmarks.
Downstream Use [optional]
This model can be fine-tuned for specific reasoning tasks or integrated into larger applications that require complex reasoning capabilities.
Out-of-Scope Use
This model should not be used for generating malicious content or for tasks that could cause harm.
Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
Trained with 17k long CoT training samples, the Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of math and coding benchmarks
Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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