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---
language:
- en
license: apache-2.0
base_model:
- HuggingFaceTB/SmolLM2-1.7B-Instruct
datasets:
- allenai/tulu-3-sft-mixture
- allenai/llama-3.1-tulu-3-8b-preference-mixture
model-index:
- name: SmolLM2-MedIT-Upscale-2B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 64.29
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/SmolLM2-MedIT-Upscale-2B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 10.51
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/SmolLM2-MedIT-Upscale-2B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 1.06
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/SmolLM2-MedIT-Upscale-2B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 1.9
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/SmolLM2-MedIT-Upscale-2B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 2.45
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/SmolLM2-MedIT-Upscale-2B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 10.78
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/SmolLM2-MedIT-Upscale-2B
      name: Open LLM Leaderboard
---
# SmolLM2-MedIT-Upscale-2B

**Model Summary**

SmolLM2-MedIT-Upscale-2B is an expanded version of the [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) model, increasing its parameter count to 2 billion. This expansion was achieved by doubling the number of heads in the `q_proj`, `k_proj`, `v_proj`, and `o_proj` layers, resulting in vectors of length 4096, compared to 2048 in the original model.

**Purpose of Expansion**

This model was developed to test the hypothesis that self-attention layers do not extend the "memory" of the model. By broadening the attention layers, we aim to observe the impact on the model's performance and memory capabilities.

## Training Status

This model underwent instruction fine-tuning for 8,800 steps using a batch size of 4, gradient accumulation for 32 steps, a maximum sequence length of 1,280, and a learning rate of 1e-5. Additionally, it was fine-tuned with 1,600 steps of DPO under the same configuration.

**Note**:
The model has undergone preliminary training focused on assessing the effects of the expanded attention layers. It is not fully trained to its maximum potential. We encourage the community to contribute to its further training; pull requests are welcome.

## Analysis of Expanded Layers

During fine-tuning, we analyzed the changes in the new parameters of the expanded layers:

- **Minimum percentage of new parameters that changed:**
  - `q_proj`: 62.17%
  - `k_proj`: 37.85%
  - `v_proj`: 99.99%
  - `o_proj`: 99.98%

- **Maximum percentage of new parameters that changed:**
  - `q_proj`: 98.86%
  - `k_proj`: 97.99%
  - `v_proj`: 99.99%
  - `o_proj`: 99.99%

- **Average change in new parameters after fine-tuning:**
  - `q_proj`: 1.838e-07
  - `k_proj`: 2.277e-07
  - `v_proj`: 6.490e-07
  - `o_proj`: 3.924e-07

These results are illustrated in the following charts:

![Percentage of Change](percent_of_change.png)
![Average Parameter Change](mean_difference.png)

## Usage

To utilize this model, follow the instructions provided for the original SmolLM2-1.7B-Instruct model, adjusting for the increased parameter size.

## Contributing

We welcome contributions to further train and evaluate this model. Please submit pull requests with your improvements.

## License

This model is licensed under the Apache 2.0 License.

## Citation

If you use this model in your research, please cite it as follows:

```
@misc{SmolLM2-MedIT-Upscale-2B,
  author = {Mariusz Kurman, MedIT Solutions},
  title = {SmolLM2-MedIT-Upscale-2B: An Expanded Version of SmolLM2-1.7B-Instruct},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/meditsolutions/SmolLM2-MedIT-Upscale-2B},
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_meditsolutions__SmolLM2-MedIT-Upscale-2B)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |15.17|
|IFEval (0-Shot)    |64.29|
|BBH (3-Shot)       |10.51|
|MATH Lvl 5 (4-Shot)| 1.06|
|GPQA (0-shot)      | 1.90|
|MuSR (0-shot)      | 2.45|
|MMLU-PRO (5-shot)  |10.78|