MerantixMomentum
/

acip_qwen25_7b

@@ -1,104 +1,89 @@
----
-license: apache-2.0
-datasets:
-- allenai/c4
-language:
-- zho
-- eng
-- fra
-- spa
-- por
-- deu
-- ita
-- rus
-- jpn
-- kor
-- vie
-- tha
-- ara
-metrics:
-- perplexity
-- accuracy
-base_model:
-- Qwen/Qwen2.5-7B
-pipeline_tag: text-generation
-library_name: transformers
----
-<div align="center">
-  <img width="30%" alt="logo" src="https://imgur.com/A0MCHPq.png">
-</div>
-<div align="center">
-    <a href="https://github.com/merantix-momentum/acip"><img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white.svg" alt="github" style="display: inline-block; vertical-align: middle;"></a>
-    <a href="https://arxiv.org/abs/2502.01717"><img src="https://img.shields.io/badge/arXiv-2502.01717-b31b1b.svg" alt="arxiv" style="display: inline-block; vertical-align: middle;"></a>
-    <a href="https://acip.merantix-momentum.com/"><img alt="website" src="https://img.shields.io/website/https/acip.merantix-momentum.com.svg?down_color=red&down_message=offline&up_message=online" style="display: inline-block; vertical-align: middle;"></a>
-</div>
-<h2 align="center">
-    <p> [
-        <a href="https://github.com/merantix-momentum/acip">🤖 GitHub</a> |
-        <a href="https://arxiv.org/abs/2502.01717">📄 Paper</a> |
-        <a href="https://acip.merantix-momentum.com/">🌐 Website</a>
-        ]
-    </p>
-</h2>
-<h1 align="center">
-    <p>ACIP applied to Qwen/Qwen2.5-7B</p>
-</h1>
-This model repository is part of the ACIP Project and provides a compressible version of [`Qwen/Qwen2.5-7B`](https://huggingface.co/Qwen/Qwen2.5-7B). For more details, please visit our [code repo](https://github.com/merantix-momentum/acip).
-# Quick Start
-Just load the ACIP model via `from_pretrained`:
-```python
-from transformers import AutoModel
-model = AutoModel.from_pretrained("MerantixMomentum/acip_qwen25_7b", trust_remote_code=True)
-```
-This will download and create a fully parameterized ACIP model that can be pruned to any compression ratio you wish.
-For example,
-```python
-model.prune_model_by_score(compression_ratio=0.4)
-```
-will prune `model` to 40% if its original size measured in number of parameters, i.e., 60% compression rate.
-A unique feature of ACIP is that this operation is revertible in the sense that you can rerun `model.prune_model_by_score` as often as you like to evaluate your model at different sizes. Finally, you can "commit" to a certain ratio and run
-```python
-model.compress()
-```
-which will discard all pruned mask values of compressible linear layers.
-Now the model is actually compressed and you should observe a significant decrease of memory usage (this step is not revertible without reloading the ACIP model).
-If you like, you can also run
-```python
-model.quantize()
-```
-to save even more memory (we have only tested 4bit quantization with `bitsandbytes`, but you could also customize this).
-**🚀 That's it! You can now use your compressed model for inference or fine-tuning as any other Causal Language Model from 🤗 transformers.**
-**Note**: The parameter `compression_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all.
-# Dependencies
-To run an ACIP model from our hub, you only need minimal dependencies, namely `torch`, `transformers`, `peft`, and optionally, `bitsandbytes` in case you want to quantize your model.
-See [requirements.txt](requirements.txt) for pip-installable dependencies with exact version pins (newer version should work as well).
-# License
-This model is released under the apache-2.0 license.
-# Citation
-When using or referring to this model, please cite our [paper](https://arxiv.org/abs/2502.01717):
-```bibtex
-@article{mxm2025acip,
-  title={Choose Your Model Size: Any Compression by a Single Gradient Descent},
-  author={M. Genzel, P. Putzky, P. Zhao, S. Schulze, M. Mollenhauer, R. Seidel, S. Dietzel, T. Wollmann},
-  year={2025},
-  journal={Preprint arXiv:2502.01717}
-}
-```

+---
+license: apache-2.0
+datasets: ['allenai/c4']
+language: ['zho', 'eng', 'fra', 'spa', 'por', 'deu', 'ita', 'rus', 'jpn', 'kor', 'vie', 'tha', 'ara']
+metrics: ['perplexity', 'accuracy']
+tags: ['acip', 'pytorch']
+base_model:
+- Qwen/Qwen2.5-7B
+pipeline_tag: text-generation
+library_name: transformers
+---
+<div align="center">
+  <img width="30%" alt="logo" src="https://imgur.com/A0MCHPq.png">
+</div>
+<div align="center">
+    <a href="https://github.com/merantix-momentum/acip"><img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white.svg" alt="github" style="display: inline-block; vertical-align: middle;"></a>
+    <a href="https://arxiv.org/abs/2502.01717"><img src="https://img.shields.io/badge/arXiv-2502.01717-b31b1b.svg" alt="arxiv" style="display: inline-block; vertical-align: middle;"></a>
+    <a href="https://acip.merantix-momentum.com/"><img alt="website" src="https://img.shields.io/website/https/acip.merantix-momentum.com.svg?down_color=red&down_message=offline&up_message=online" style="display: inline-block; vertical-align: middle;"></a>
+</div>
+<h2 align="center">
+    <p> [
+        <a href="https://github.com/merantix-momentum/acip">🤖 GitHub</a> |
+        <a href="https://arxiv.org/abs/2502.01717">📄 Paper</a> |
+        <a href="https://acip.merantix-momentum.com/">🌐 Website</a>
+        ]
+    </p>
+</h2>
+<h1 align="center">
+    <p>ACIP applied to Qwen/Qwen2.5-7B</p>
+</h1>
+This model repository is part of the ACIP Project and provides a compressible version of [`Qwen/Qwen2.5-7B`](https://huggingface.co/Qwen/Qwen2.5-7B). For more details, please visit our [code repo](https://github.com/merantix-momentum/acip).
+# Quick Start
+Just load the ACIP model via `from_pretrained`:
+```python
+from transformers import AutoModel
+model = AutoModel.from_pretrained("MerantixMomentum/acip_qwen25_7b", trust_remote_code=True)
+```
+This will download and create a fully parameterized ACIP model that can be pruned to any compression rate you wish.
+For example,
+```python
+model.prune_model_by_score(size_ratio=0.4)
+```
+will prune `model` to 40% if its original size measured in number of parameters, i.e., 60% compression rate.
+A unique feature of ACIP is that this operation is revertible in the sense that you can rerun `model.prune_model_by_score` as often as you like to evaluate your model at different sizes. Finally, you can "commit" to a certain ratio and run
+```python
+model.compress()
+```
+which will discard all pruned mask values of compressible linear layers.
+Now the model is actually compressed and you should observe a significant decrease of memory usage (this step is not revertible without reloading the ACIP model).
+If you like, you can also run
+```python
+model.quantize()
+```
+to save even more memory (we have only tested 4bit quantization with `bitsandbytes`, but you could also customize this).
+**🚀 That's it! You can now use your compressed model for inference or fine-tuning as any other Causal Language Model from 🤗 transformers.**
+**Note**: The parameter `size_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all. Alternatively, you can also set `compression_rate` in `prune_model_by_score`, which is equivalent to `size_ratio = 1.0 - compression_rate`.
+# Dependencies
+To run an ACIP model from our hub, you only need minimal dependencies, namely `torch`, `transformers`, `peft`, and optionally, `bitsandbytes` in case you want to quantize your model.
+See [requirements.txt](requirements.txt) for pip-installable dependencies with exact version pins (newer version should work as well).
+# License
+This model is released under the apache-2.0 license.
+# Citation
+When using or referring to this model, please cite our [paper](https://arxiv.org/abs/2502.01717):
+```bibtex
+@article{mxm2025acip,
+  title={Choose Your Model Size: Any Compression by a Single Gradient Descent},
+  author={M. Genzel, P. Putzky, P. Zhao, S. Schulze, M. Mollenhauer, R. Seidel, S. Dietzel, T. Wollmann},
+  year={2025},
+  journal={Preprint arXiv:2502.01717}
+}
+```