Add pipeline tag: text-generation

This PR adds the `pipeline_tag: text-generation` to the model card metadata. This tag is crucial for discoverability on the Hugging Face Hub, allowing researchers to easily find this model when searching for text generation models.

README.md

@@ -1,46 +1,47 @@
----
-license: apache-2.0
-language:
-- en
-library_name: transformers
----
-
-# Introduction
-
-This repository contains the checkpoints of ICLR 2025 paper **[“Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models](https://arxiv.org/pdf/2411.03884)”.**
-In this work, we introduce a novel activation function called **Polynomial Composition (PolyCom)**, which enhances the expressiveness of large language models (LLMs) through dynamic polynomial compositions. Our method significantly improves the performance of dense and mixture of experts (MoE) models across a variety of downstream tasks, without adding significant computational overhead.
-
-# Datasets and Training
-
-We use the [RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) dataset and pretrain the PolyCom model on 250B tokens. For more training details, please refer to [the source code](https://github.com/BryceZhuo/PolyCom).
-
-
-# Inference
-
-Here is an example of how to use the PolyCom model for inference:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model = AutoModelForCausalLM.from_pretrained(path_of_model, device_map="cuda",trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained(path_of_model, padding_side="right",trust_remote_code=True)
-
-prompt = "Hello, my name is"
-input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
-
-greedy_output = model.generate(input_ids)
-print(tokenizer.decode(greedy_output[0], skip_special_tokens=True))
-```
-
-
-# Citing this work
-
-If you find this work helpful or use it in your research, please consider citing our paper:
-```bibtex
-@inproceedings{zhuo2025polycom,
-  title={Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models},
-  author={Zhijian Zhuo and Ya Wang and Yutao Zeng and Xiaoqing Li and Xun Zhou and Jinwen Ma},
-  booktitle={ICLR 2025},
-  year={2025}
-}
-```
+---
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+# Introduction
+
+This repository contains the checkpoints of the ICLR 2025 paper **[“Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models](https://arxiv.org/pdf/2411.03884)”.**  This work introduces **Polynomial Composition (PolyCom)**, a novel activation function that enhances the expressiveness of large language models (LLMs). PolyCom significantly improves the performance of dense and mixture of experts (MoE) models across various downstream tasks, without adding significant computational overhead.
+
+
+# Datasets and Training
+
+We use the [RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) dataset and pretrain the PolyCom model on 250B tokens. For more training details, please refer to [the source code](https://github.com/BryceZhuo/PolyCom).
+
+
+# Inference
+
+Here is an example of how to use the PolyCom model for inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(path_of_model, device_map="cuda",trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(path_of_model, padding_side="right",trust_remote_code=True)
+
+prompt = "Hello, my name is"
+input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
+
+greedy_output = model.generate(input_ids)
+print(tokenizer.decode(greedy_output[0], skip_special_tokens=True))
+```
+
+
+# Citing this work
+
+If you find this work helpful or use it in your research, please consider citing our paper:
+```bibtex
+@inproceedings{zhuo2025polycom,
+  title={Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models},
+  author={Zhijian Zhuo and Ya Wang and Yutao Zeng and Xiaoqing Li and Xun Zhou and Jinwen Ma},
+  booktitle={ICLR 2025},
+  year={2025}
+}
+```