Add library_name and pipeline_tag to metadata

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by nielsr HF Staff - opened
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  1. README.md +6 -4
README.md CHANGED
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  ---
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  license: apache-2.0
 
 
 
 
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  ---
 
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  **Paper**: [https://arxiv.org/pdf/2502.07780](https://arxiv.org/pdf/2502.07780)
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  **Code**: https://github.com/IST-DASLab/DarwinLM
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  **Models**: [DarwinLM-2.7B](https://huggingface.co/Shengkun/DarwinLM-2.7B), [DarwinLM-4.6B](https://huggingface.co/Shengkun/DarwinLM-4.6B), [DarwinLM-8.4B](https://huggingface.co/Shengkun/DarwinLM-8.4B)
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  ---
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  This repository contains the weights of DarwinLM, an evolutionary structured pruning methods for large language models, as introduced in our paper. DarwinLM builds upon an evolutionary search process, generating multiple offspring models in each generation through mutation, and selecting the fittest for survival.
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- ```
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  # Please add trust_remote_code=True as the repo includes custom code to load and run DarwinLM
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  model = AutoModelForCausalLM.from_pretrained("Shengkun/DarwinLM-2.7B", trust_remote_code=True)
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  ```
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  ## Downstream Tasks
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  **2.7B**
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  | Method | Param. | SciQ | PIQA | WG | ArcE | ArcC | HS | LogiQA | BoolQ | Avg |
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  | | **OLMO-0424 (2.05T)** | 7B | 96.1 | 80.1 | 72.1 | 73.8 | 49.2 | 78.0 | 29.3 | 80.8 | 52.1 | 67.9 |
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  | | *DarwinLM (10.0B)* | 8.4B | 89.5 | 78.1 | 70.7 | 79.6 | 57.6 | 74.9 | 33.5 | 73.9 | 57.9 | 68.4 |
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-
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  ## Bibtex
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  ```
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  @article{tang2025darwinlm,
 
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  ---
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  license: apache-2.0
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tags:
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+ - code-generation
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  ---
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+
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  **Paper**: [https://arxiv.org/pdf/2502.07780](https://arxiv.org/pdf/2502.07780)
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  **Code**: https://github.com/IST-DASLab/DarwinLM
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  **Models**: [DarwinLM-2.7B](https://huggingface.co/Shengkun/DarwinLM-2.7B), [DarwinLM-4.6B](https://huggingface.co/Shengkun/DarwinLM-4.6B), [DarwinLM-8.4B](https://huggingface.co/Shengkun/DarwinLM-8.4B)
 
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  ---
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  This repository contains the weights of DarwinLM, an evolutionary structured pruning methods for large language models, as introduced in our paper. DarwinLM builds upon an evolutionary search process, generating multiple offspring models in each generation through mutation, and selecting the fittest for survival.
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+ ```python
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  # Please add trust_remote_code=True as the repo includes custom code to load and run DarwinLM
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  model = AutoModelForCausalLM.from_pretrained("Shengkun/DarwinLM-2.7B", trust_remote_code=True)
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  ```
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  ## Downstream Tasks
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  **2.7B**
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  | Method | Param. | SciQ | PIQA | WG | ArcE | ArcC | HS | LogiQA | BoolQ | Avg |
 
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  | | **OLMO-0424 (2.05T)** | 7B | 96.1 | 80.1 | 72.1 | 73.8 | 49.2 | 78.0 | 29.3 | 80.8 | 52.1 | 67.9 |
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  | | *DarwinLM (10.0B)* | 8.4B | 89.5 | 78.1 | 70.7 | 79.6 | 57.6 | 74.9 | 33.5 | 73.9 | 57.9 | 68.4 |
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  ## Bibtex
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  ```
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  @article{tang2025darwinlm,