SmallDoge
/

Doge-160M-Instruct

@@ -1,199 +1,127 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- HuggingFaceTB/smoltalk
+- HuggingFaceH4/ultrafeedback_binarized
+base_model:
+- SmallDoge/Doge-160M
+language:
+- en
+pipeline_tag: question-answering
 ---
+# **Doge 160M Instruct**
+<div align="center">
+  <img src="https://huggingface.co/spaces/SmallDoge/README/resolve/main/org_icon.png" width="100%" alt="SmallDoge" />
+</div>
+<hr>
+<div align="center">
+  <a href="https://arxiv.org/abs/2412.11834" target="_blank" style="margin: 2px;">
+    <img alt="arXiv" src="https://img.shields.io/static/v1?label=arXiv&message=2412.11834&color=B31B1B&logo=arXiv" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/SmallDoges/small-doge" target="_blank" style="margin: 2px;">
+    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-SmallDoge-181717?logo=github" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/SmallDoge" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-SmallDoge-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/SmallDoges/small-doge/blob/main/LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-Apache--2.0-blue.svg" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by [SmallDoge](https://huggingface.co/SmallDoge) community, for detailed algorithm and model architecture, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), all training details and code are publicly available on the [small-doge](https://github.com/SmallDoges/small-doge) repository.
 ## Uses
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, TextStreamer
+tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-160M-Instruct")
+model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-160M-Instruct", trust_remote_code=True)
+generation_config = GenerationConfig(
+      max_new_tokens=100,
+      use_cache=True,
+      do_sample=True,
+      temperature=0.8,
+      top_p=0.9,
+      repetition_penalty=1.0
+)
+steamer = TextStreamer(
+      tokenizer=tokenizer,
+      skip_prompt=True
+)
+prompt = "Hi, how are you doing today?"
+conversation = [
+      {"role": "user", "content": prompt}
+]
+inputs = tokenizer.apply_chat_template(
+    conversation=conversation,
+    tokenize=True,
+    return_tensors="pt",
+)
+outputs = model.generate(
+    inputs,
+    tokenizer=tokenizer,
+    generation_config=generation_config,
+    streamer=steamer
+)
+```
+## Model Details
+We build the Doge-Instruct by first SFT on [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) and then DPO on [UltraFeedback Binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
+> TODO: The larger model is under training and will be uploaded soon.
+**SFT**:
+| Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
+|---|---|---|---|---|---|---|
+| [Doge-20M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-20M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 8e-4 | 0.25M | bfloat16 |
+| [Doge-60M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-60M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 6e-4 | 0.25M | bfloat16 |
+| [Doge-160M-Instruct-SFT](https://huggingface.co/SmallDoge/Doge-160M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 4e-4 | 0.25M | bfloat16 |
+**DPO**:
+| Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
+|---|---|---|---|---|---|---|
+| [Doge-20M-Instruct](https://huggingface.co/SmallDoge/Doge-20M-Instruct) | [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) | 2 | 1024 | 8e-5 | 0.125M | bfloat16 |
+| [Doge-60M-Instruct](https://huggingface.co/SmallDoge/Doge-60M-Instruct) | [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) | 2 | 1024 | 6e-5 | 0.125M | bfloat16 |
+| [Doge-160M-Instruct](https://huggingface.co/SmallDoge/Doge-160M-Instruct) | [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) | 2 | 1024 | 4e-5 | 0.125M | bfloat16 |
+**Procedure**:
+**SFT**:
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/loser_cheems/huggingface/runs/0jht5dro)
+**DPO**:
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/loser_cheems/huggingface/runs/m5onn07v)
+**Environment**:
+- Image: nvcr.io/nvidia/pytorch:24.12-py3
+- Hardware: 1x NVIDIA RTX 4090
+- Software: Transformers, TRL
+## Citation
+```bibtex
+@misc{shi2024wonderfulmatrices,
+      title={Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture},
+      author={Jingze Shi and Bingheng Wu},
+      year={2024},
+      eprint={2412.11834},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2412.11834},
+}
+```