---
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!](https://hf.co/papers/2502.07374). The code is available at [https://github.com/NovaSky-AI/SkyThought](https://github.com/NovaSky-AI/SkyThought).

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## Model Details

### Model Description

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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]

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- **Repository:** [https://github.com/NovaSky-AI/SkyThought](https://github.com/NovaSky-AI/SkyThought)
- **Paper [optional]:** [LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!](https://hf.co/papers/2502.07374)
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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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]

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This model can be fine-tuned for specific reasoning tasks or integrated into larger applications that require complex reasoning capabilities.

### Out-of-Scope Use

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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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[More Information Needed]

### Recommendations

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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

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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

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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