Update README.md

README.md

@@ -6,98 +6,24 @@ datasets:
 language:
 - en
 pipeline_tag: text-generation
-tags:
-- pt
-- doge
 ---
 
+# **Doge 320M checkpoint**
 
-# **Doge 320M**
+![wsd_scheduler](./wsd_scheduler.png)
 
-<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://discord.gg/P2yYH95N" target="_blank" style="margin: 2px;">
-    <img alt="Discord" src="https://img.shields.io/badge/Discord-Small%20Doges-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <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://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 `wsd_scheduler` as the training scheduler, which divides the learning rate into three stages: `warmup`, `stable`, and `decay`. It allows us to continue training on any new dataset from any checkpoint in the `stable stage` without spikes of the training.
 
-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.
+Here are the initial learning rates required to continue training at each checkpoint:
 
+- **[Doge-20M](https://huggingface.co/SmallDoge/Doge-20M-checkpoint)**: 8e-3
+- **[Doge-60M](https://huggingface.co/SmallDoge/Doge-60M-checkpoint)**: 6e-3
+- **[Doge-160M](https://huggingface.co/SmallDoge/Doge-160M-checkpoint)**: 4e-3
+- **[Doge-320M](https://huggingface.co/SmallDoge/Doge-320M-checkpoint)**: 2e-3
 
-## Uses
-
-```python
->>> from transformers import AutoTokenizer, AutoModelForCausalLM
-
->>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-320M")
->>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-320M", trust_remote_code=True)
->>> inputs = tokenizer("Hey how are you doing?", return_tensors="pt")
-
->>> out = model.generate(**inputs, max_new_tokens=100)
->>> print(tokenizer.batch_decode(out))
-```
-
-
-## Model Details
-
-We build the Doge by doing Per-Training on [Smollm-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus). If you want to continue pre-training this model, you can find the unconverged checkpoint [here](https://huggingface.co/SmallDoge/Doge-160M-checkpoint). These models has not been fine-tuned for instruction, the instruction model is [here](https://huggingface.co/SmallDoge/Doge-160M-Instruct).
-
-
-**Pre-Training**:
-
-| Model | Training Data | Steps | Content Length | Tokens | LR | Batch Size | Precision | RTX 4090 GPU hours |
-|---|---|---|---|---|---|---|---|---|
-| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 8k  | 2048 | 4B | 8e-3 | 0.5M | bfloat16 | 14 |
-| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 16k  | 2048 | 16B | 6e-3 | 1M | bfloat16 | 128 |
-| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 24k  | 2048 | 32B | 4e-3 | 1.5M | bfloat16 | 522 |
-| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 32k  | 2048 | 64B | 2e-3 | 2M | bfloat16 | 1856 |
-
-**Evaluation**:
-
-| Model | MMLU | TriviaQA | ARC | PIQA | HellaSwag | OBQA | Winogrande | tokens / s on i7-11 CPU |
-|---|---|---|---|---|---|---|---|---|
-| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M) | 25.4 | 0.03 | 29.8 | 58.4 | 27.3 | 25.6 | 50.2 | 142 |
-| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M) | 26.4 | 0.2 | 37.9 | 61.4 | 31.5 | 28.0 | 50.8 | 62 |
-| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M) | 29.2 | 4.8 | 44.4 | 66.3 | 38.7 | 34.4 | 52.2 | 28 |
-| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M) | 33.8 | 9.4 | 52.1 | 69.9 | 46.5 | 37.9 | 55.0 | 16 |
-
-> [!NOTE]
-> All evaluations are done using five-shot settings, without additional training on the benchmarks.
-
-**Procedure**:
-
-[<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/y18ty3sh) 
-
-
-**Environment**:
-
-- Image: nvcr.io/nvidia/pytorch:24.12-py3
-- Hardware: 1x NVIDIA RTX 4090
-- Software: Transformers
-
-
-## 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}, 
-}
-```
+| Model | Learning Rate | Schedule | Warmup Steps | Stable Steps |
+|-------|---------------|----------|--------------|--------------|
+| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M-checkpoint) | 8e-3 | wsd_scheduler | 800 | 6400 |
+| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M-checkpoint) | 6e-3 | wsd_scheduler | 1600 | 12800 |
+| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M-checkpoint) | 4e-3 | wsd_scheduler | 2400 | 19200 |
+| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M-checkpoint) | 2e-3 | wsd_scheduler | 3200 | 25600 |