PowerInfer
/

SmallThinker-3B-Preview

@@ -1,106 +1,118 @@
----
-datasets:
-- PowerInfer/QWQ-LONGCOT-500K
-- PowerInfer/LONGCOT-Refine-500K
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-pipeline_tag: text-generation
-language:
-- en
-library_name: transformers
----
-# SmallThinker-3B-preview
-We introduce **SmallThinker-3B-preview**, a new model fine-tuned from the [Qwen2.5-3b-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model.
-Now you can directly deploy SmallThinker On your phones with [PowerServe](https://github.com/powerserve-project/PowerServe).
-## Benchmark Performance
-| Model | AIME24 | AMC23 | GAOKAO2024_I | GAOKAO2024_II | MMLU_STEM | AMPS_Hard | math_comp |
-|---------|--------|-------|--------------|---------------|-----------|-----------|-----------|
-| Qwen2.5-3B-Instruct | 6.67 | 45 | 50 | 35.8 | 59.8 | - | - |
-| SmallThinker | 16.667 | 57.5 | 64.2 | 57.1 | 68.2 | 70 | 46.8 |
-| GPT-4o | 9.3 | - | - | - | 64.2 | 57 | 50 |
-Limitation: Due to SmallThinker's current limitations in instruction following, for math_comp we adopt a more lenient evaluation method where only correct answers are required, without constraining responses to follow the specified AAAAA format.
-Colab Link: [Colab](https://colab.research.google.com/drive/182q600at0sVw7uX0SXFp6bQI7pyjWXQ2?usp=sharing)
-## Intended Use Cases
-SmallThinker is designed for the following use cases:
-1.  **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices.
-2.  **Draft Model for QwQ-32B-Preview:** SmallThinker can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. From my test, in llama.cpp we can get 70% speedup (from 40 tokens/s to 70 tokens/s).
-## Training Details
-The model was trained using 8 H100 GPUs with a global batch size of 16. The specific configuration is as follows:
-The SFT (Supervised Fine-Tuning) process was conducted in two phases:
-1. First Phase:
-   - Used only the PowerInfer/QWQ-LONGCOT-500K dataset
-   - Trained for 1.5 epochs
-```
-### model
-model_name_or_path: /home/syx/Qwen2.5-3B-Instruct
-### method
-stage: sft
-do_train: true
-finetuning_type: full
-deepspeed: examples/deepspeed/ds_z3_config.json
-### dataset
-dataset: o1-v2
-template: qwen
-neat_packing: true
-cutoff_len: 16384
-overwrite_cache: true
-preprocessing_num_workers: 16
-### output
-output_dir: saves/qwen2-01-qat/full/sft
-logging_steps: 1
-save_steps: 1000
-plot_loss: true
-overwrite_output_dir: true
-```
-2. Second Phase:
-   - Combined training with PowerInfer/QWQ-LONGCOT-500K and PowerInfer/LONGCOT-Refine datasets
-   - Continued training for 2 additional epochs
-```
-### model
-model_name_or_path: saves/qwen2-01-qat/full/sft/checkpoint-24000
-### method
-stage: sft
-do_train: true
-finetuning_type: full
-deepspeed: examples/deepspeed/ds_z3_config.json
-### dataset
-dataset: o1-v2, o1-v3
-template: qwen
-neat_packing: true
-cutoff_len: 16384
-overwrite_cache: true
-preprocessing_num_workers: 16
-### output
-output_dir: saves/qwen2-01-qat/full/sft
-logging_steps: 1
-save_steps: 1000
-plot_loss: true
-overwrite_output_dir: true
-```
-## Limitations & Disclaimer
-Please be aware of the following limitations:
-*   **Language Limitation:** The model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking.
-*   **Limited Knowledge:** Due to limited SFT data and the model's relatively small scale, its reasoning capabilities are constrained by its knowledge base.
-*   **Unpredictable Outputs:** The model may produce unexpected outputs due to its size and probabilistic generation paradigm. Users should exercise caution and validate the model's responses.
 *   **Repetition Issue:** The model tends to repeat itself when answering high-difficulty questions. Please increase the `repetition_penalty` to mitigate this issue.

+---
+datasets:
+- PowerInfer/QWQ-LONGCOT-500K
+- PowerInfer/LONGCOT-Refine-500K
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+library_name: transformers
+---
+# SmallThinker-3B-preview
+We introduce **SmallThinker-3B-preview**, a new model fine-tuned from the [Qwen2.5-3b-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model.
+Now you can directly deploy SmallThinker On your phones with [PowerServe](https://github.com/powerserve-project/PowerServe).
+## Benchmark Performance
+| Model | AIME24 | AMC23 | GAOKAO2024_I | GAOKAO2024_II | MMLU_STEM | AMPS_Hard | math_comp |
+|---------|--------|-------|--------------|---------------|-----------|-----------|-----------|
+| Qwen2.5-3B-Instruct | 6.67 | 45 | 50 | 35.8 | 59.8 | - | - |
+| SmallThinker | 16.667 | 57.5 | 64.2 | 57.1 | 68.2 | 70 | 46.8 |
+| GPT-4o | 9.3 | - | - | - | 64.2 | 57 | 50 |
+Limitation: Due to SmallThinker's current limitations in instruction following, for math_comp we adopt a more lenient evaluation method where only correct answers are required, without constraining responses to follow the specified AAAAA format.
+Colab Link: [Colab](https://colab.research.google.com/drive/182q600at0sVw7uX0SXFp6bQI7pyjWXQ2?usp=sharing)
+## Intended Use Cases
+SmallThinker is designed for the following use cases:
+1.  **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices.
+2.  **Draft Model for QwQ-32B-Preview:** SmallThinker can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. From my test, in llama.cpp we can get 70% speedup (from 40 tokens/s to 70 tokens/s).
+## Training Details
+The model was trained using 8 H100 GPUs with a global batch size of 16. The specific configuration is as follows:
+The SFT (Supervised Fine-Tuning) process was conducted in two phases:
+1. First Phase:
+   - Used only the PowerInfer/QWQ-LONGCOT-500K dataset
+   - Trained for 1.5 epochs
+```
+### model
+model_name_or_path: /home/syx/Qwen2.5-3B-Instruct
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z3_config.json
+### dataset
+dataset: o1-v2
+template: qwen
+neat_packing: true
+cutoff_len: 16384
+overwrite_cache: true
+preprocessing_num_workers: 16
+### output
+output_dir: saves/qwen2-01-qat/full/sft
+logging_steps: 1
+save_steps: 1000
+plot_loss: true
+overwrite_output_dir: true
+```
+2. Second Phase:
+   - Combined training with PowerInfer/QWQ-LONGCOT-500K and PowerInfer/LONGCOT-Refine datasets
+   - Continued training for 2 additional epochs
+```
+### model
+model_name_or_path: saves/qwen2-01-qat/full/sft/checkpoint-24000
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z3_config.json
+### dataset
+dataset: o1-v2, o1-v3
+template: qwen
+neat_packing: true
+cutoff_len: 16384
+overwrite_cache: true
+preprocessing_num_workers: 16
+### output
+output_dir: saves/qwen2-01-qat/full/sft
+logging_steps: 1
+save_steps: 1000
+plot_loss: true
+overwrite_output_dir: true
+```
+## Limitations & Disclaimer
+Please be aware of the following limitations:
+*   **Language Limitation:** The model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking.
+*   **Limited Knowledge:** Due to limited SFT data and the model's relatively small scale, its reasoning capabilities are constrained by its knowledge base.
+*   **Unpredictable Outputs:** The model may produce unexpected outputs due to its size and probabilistic generation paradigm. Users should exercise caution and validate the model's responses.
 *   **Repetition Issue:** The model tends to repeat itself when answering high-difficulty questions. Please increase the `repetition_penalty` to mitigate this issue.