Update README.md

README.md

@@ -11,14 +11,25 @@ Both [**YuLan-Mini**](https://huggingface.co/yulan-team/YuLan-Mini) and **YuLan-
 
 This version includes the optimizer, allowing you to resume training using the Hugging Face Trainer and DeepSpeed Universal Checkpoint.
 
-For easier inference and deployment, we merged the re-parameterized added parameters and scaling factors into the final released models ([**YuLan-Mini**](https://huggingface.co/yulan-team/YuLan-Mini) and **YuLan-Mini-Intermediate-4K**), enabling it to run on the Llama architecture. However, these parameters are still retained in the intermediate checkpoints from the training process.
+| Stage     | Curriculum Phase | 4K Context                  | 28K Context | Optimizer | Inference Arch | LAMBADA `Acc` | GSM8K `Acc` | HumanEval `pass@1` |
+|-----------|------------------|-----------------------------|-------------|-----------|----------------|-------------|-------------|--------------------|
+| Stable    | 5                | [YuLan-Mini-Phase5](https://huggingface.co/yulan-team/YuLan-Mini-Phase5) |       | | `yulanmini` | 53.85 | 3.41 | 12.26 |
+| Stable    | 10               | [YuLan-Mini-Phase10](https://huggingface.co/yulan-team/YuLan-Mini-Phase10) |       | | `yulanmini` | 55.00 | 9.57 | 15.95 |
+| Stable    | 15               | [YuLan-Mini-Phase15](https://huggingface.co/yulan-team/YuLan-Mini-Phase15) |       | | `yulanmini` | 55.81 | 13.81 | 16.99 |
+| Stable    | 20               | [YuLan-Mini-Phase20](https://huggingface.co/yulan-team/YuLan-Mini-Phase20) | | ✅ | `yulanmini` | 55.81 | 21.39 | 20.79 |
+| Stable    | 25 (1T tokens)   | [YuLan-Mini-Before-Annealing](https://huggingface.co/yulan-team/YuLan-Mini-Before-Annealing) | | ✅ | `yulanmini` | 55.67 | 29.94 | 34.06 |
+| | | | | | | | |
+| Annealing | 26               | YuLan-Mini-4K  |             | | `llama`\* | 64.72 | 66.65 | 61.60 |
+| Annealing | 27               |                             | [YuLan-Mini](https://huggingface.co/yulan-team/YuLan-Mini)  | | `llama`\* | 65.67 | 68.46 | 64.00 |
+
+\*: For easier inference and deployment, we merged the re-parameterized added parameters and scaling factors into the final released models ([**YuLan-Mini**](https://huggingface.co/yulan-team/YuLan-Mini) and **YuLan-Mini-Intermediate-4K**), enabling it to run on the Llama architecture. However, these parameters are still retained in the intermediate checkpoints from the training process.
 
 ## What you can do with these pre-training resources
 
 1. **Pre-train** your own LLM. You can use [our data](https://huggingface.co/yulan-team/YuLan-Mini-Datasets) and curriculum to train a model that's just as powerful as YuLan-Mini.
 2. Perform your own **learning rate annealing**. During the annealing phase, YuLan-Mini's learning ability is at its peak. You can resume training from [the checkpoint before annealing](https://huggingface.co/yulan-team/YuLan-Mini-Before-Annealing) and use your own dataset for learning rate annealing.
-3. **Fine-tune** the Instruct version of the LLM. You can use the YuLan-Mini base model to train your own Instruct version.
-4. **Training dynamics** research. You can use YuLan-Mini's intermediate checkpoints to explore internal changes during the pre-training process.
+3. **Fine-tune** the Instruct version of the LLM. You can use the [YuLan-Mini](https://huggingface.co/yulan-team/YuLan-Mini) base model to train your own Instruct version.
+4. **Training dynamics** research. You can use YuLan-Mini's [intermediate checkpoints](https://huggingface.co/collections/yulan-team/yulan-mini-676d214b24376739b00d95f3) to explore internal changes during the pre-training process.
 5. **Synthesize** your own data. You can use YuLan-Mini's [data pipeline](https://github.com/RUC-GSAI/YuLan-Mini) to clean and generate your own dataset.