metadata
library_name: transformers
license: mit
tags: []
pipeline_tag: text-generation
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Sky-T1-32B-Preview is a 32B parameter model described in the paper [LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!](https://hf.co/papers/2502.07374)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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 Team]
- **Funded by [optional]:** [Berkeley Sky Computing Lab]
- **Shared by [optional]:** [NovaSky-AI]
- **Model type:** [Qwen2ForCausalLM]
- **Language(s) (NLP):** [English]
- **License:** [MIT]
- **Finetuned from model [optional]:** [Qwen2.5-32B-Instruct]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** 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]:** http://164.152.23.196:3000/
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Text generation, reasoning
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
Text generation, reasoning
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Malicious uses
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "NovaSky-AI/Sky-T1-32B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
prompt = "This is a test prompt"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(**inputs)
decoded_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
Training Details
Training Data
https://huggingface.co/datasets/NovaSky-AI/Sky-T1_data_17k
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [bf16 mixed precision]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
@misc{sky_t1_2025,
author = {NovaSky Team},
title = {Sky-T1: Train your own O1 preview model within $450},
howpublished = {https://novasky-ai.github.io/posts/sky-t1},
note = {Accessed: 2025-01-09},
year = {2025}
}
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed] ```