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Improve model card

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This 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.

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  1. README.md +44 -20
README.md CHANGED
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  ---
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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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-
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-
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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:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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- [More Information Needed]
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  ### Training Procedure
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  #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--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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- [More Information Needed]
 
 
 
 
 
 
 
 
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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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  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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+
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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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+
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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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+
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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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+ ```