LoserCheems
/

Doge-22M

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- HuggingFaceTB/smollm-corpus
+language:
+- en
+pipeline_tag: text-generation
 ---
+# **Doge 22M**
+Doge is an ongoing research project where we aim to train a series of small language models to further explore whether the Transformer framework allows for more complex feedforward network structures, enabling the model to have fewer cache states and larger knowledge capacity.
+In addition, Doge uses Inner Function Attention with Dynamic Mask as sequence transformation and Cross Domain Mixture of Experts as state transformation. This model is trained by Jingze Shi, it only allows text input and text generation, for detailed algorithm and model architecture, please refer to [Wonderful Matrices](https://arxiv.org/abs/2407.16958), the ongoing research repository is [Doge](https://github.com/LoserCheems/Doge).
 ## Uses
+```python
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> tokenizer = AutoTokenizer.from_pretrained("LoserCheems/Doge-22M")
+>>> model = AutoModelForCausalLM.from_pretrained("LoserCheems/Doge-22M", trust_remote_code=True)
+>>> inputs = tokenizer("Hey how are you doing?", return_tensors="pt")
+>>> out = model.generate(**inputs, max_new_tokens=10)
+>>> print(tokenizer.batch_decode(out))
+```
+## Training
+### Model
+- Architecture: Architecture details refer to [Wonderful Matrices](https://arxiv.org/abs/2407.16958v5)
+- Pretraining data: [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus)
+- Pretraining steps: 5k
+- Pretraining tokens: 1B
+- Precision: bfloat16
+> NOTE: This model has not been fine-tuned for instruction
+### Environment
+- Image: nvcr.io/nvidia/pytorch:24.10-py3
+- Hardware: 1x NVIDIA RTX 4090
+- Software: Transformers
+## Citation
+```bibtex
+@misc{shi2024wonderfulmatrices,
+      title={Wonderful Matrices: More Efficient and Effective Architecture for Language Modeling Tasks},
+      author={Jingze Shi and Bingheng Wu and Lu He and Luchang Jiang},
+      year={2024},
+      eprint={2407.16958},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2407.16958},
+}
+```