Improve model card
Browse filesThis PR improves the model card by adding relevant metadata, including the pipeline tag, library name, and license. It also adds a link to the paper and the GitHub repository, as well as a basic code snippet demonstrating how to use the model.
README.md
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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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.
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- **Developed by:** [
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- **Funded by [optional]:** [
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- **Shared by [optional]:** [
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- **Model type:** [
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- **Language(s) (NLP):** [
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- **License:** [
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- **Finetuned from model [optional]:** [
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:**
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- **Paper [optional]:** [
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- **Demo [optional]:**
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## Uses
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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Use the code below to get started with the model.
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## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
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#### Training Hyperparameters
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- **Training regime:** [
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#### Speeds, Sizes, Times [optional]
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**BibTeX:**
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**APA:**
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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license: mit
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tags: []
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pipeline_tag: text-generation
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---
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```markdown
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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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)
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## Model Details
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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.
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- **Developed by:** [NovaSky Team]
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- **Funded by [optional]:** [Berkeley Sky Computing Lab]
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- **Shared by [optional]:** [NovaSky-AI]
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- **Model type:** [Qwen2ForCausalLM]
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- **Language(s) (NLP):** [English]
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- **License:** [MIT]
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- **Finetuned from model [optional]:** [Qwen2.5-32B-Instruct]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/NovaSky-AI/SkyThought
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- **Paper [optional]:** [LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!](https://hf.co/papers/2502.07374)
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- **Demo [optional]:** http://164.152.23.196:3000/
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## Uses
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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Text generation, reasoning
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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Text generation, reasoning
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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Malicious uses
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## Bias, Risks, and Limitations
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Use the code below to get started with the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "NovaSky-AI/Sky-T1-32B-Preview"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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prompt = "This is a test prompt"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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generated_ids = model.generate(**inputs)
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decoded_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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```
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## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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https://huggingface.co/datasets/NovaSky-AI/Sky-T1_data_17k
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### Training Procedure
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#### Training Hyperparameters
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- **Training regime:** [bf16 mixed precision] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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**BibTeX:**
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```
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@misc{sky_t1_2025,
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author = {NovaSky Team},
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title = {Sky-T1: Train your own O1 preview model within $450},
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howpublished = {https://novasky-ai.github.io/posts/sky-t1},
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note = {Accessed: 2025-01-09},
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year = {2025}
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}
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```
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**APA:**
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## Model Card Contact
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[More Information Needed]
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```
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