
# 🌞🚀 SOLAR-math-2x10.7_19B 

+ This model is part of MoE experimentation. The other solar models in the collection are available [here](https://huggingface.co/collections/macadeliccc/solar-moe-65a2d28e3581a68c41381d5b)

+ If you like this model, version 2 is even better! It is competitve with GPT-3.5 Turbo and Gemini Pro. It exceeds the scores of Mixtral8x7b [macadeliccc/SOLAR-math-2x10.7b-v0.2](https://huggingface.co/macadeliccc/SOLAR-math-2x10.7b-v0.2)

![solar](solar-2.png)


## 🌅 Code Example

Example also available in [colab](https://colab.research.google.com/drive/10FWCLODU_EFclVOFOlxNYMmSiLilGMBZ?usp=sharing)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

def generate_response(prompt):
    """
    Generate a response from the model based on the input prompt.

    Args:
    prompt (str): Prompt for the model.

    Returns:
    str: The generated response from the model.
    """
    # Tokenize the input prompt
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Generate output tokens
    outputs = model.generate(**inputs, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id)

    # Decode the generated tokens to a string
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return response


# Load the model and tokenizer
model_id = "macadeliccc/SOLAR-math-2x10.7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)

prompt = "Explain the proof of Fermat's Last Theorem and its implications in number theory."


print("Response:")
print(generate_response(prompt), "\n")
```

## Evaluations 

|                                   Model                                   |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[SOLAR-math-2x10.7b](https://huggingface.co/macadeliccc/SOLAR-math-2x10.7b)|   47.2|  75.18|     64.73|   45.15|  58.07|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |30.31|±  |  2.89|
|                              |       |acc_norm|30.31|±  |  2.89|
|agieval_logiqa_en             |      0|acc     |43.78|±  |  1.95|
|                              |       |acc_norm|43.93|±  |  1.95|
|agieval_lsat_ar               |      0|acc     |21.74|±  |  2.73|
|                              |       |acc_norm|19.13|±  |  2.60|
|agieval_lsat_lr               |      0|acc     |57.25|±  |  2.19|
|                              |       |acc_norm|56.47|±  |  2.20|
|agieval_lsat_rc               |      0|acc     |68.77|±  |  2.83|
|                              |       |acc_norm|68.03|±  |  2.85|
|agieval_sat_en                |      0|acc     |78.16|±  |  2.89|
|                              |       |acc_norm|79.13|±  |  2.84|
|agieval_sat_en_without_passage|      0|acc     |47.57|±  |  3.49|
|                              |       |acc_norm|44.66|±  |  3.47|
|agieval_sat_math              |      0|acc     |41.36|±  |  3.33|
|                              |       |acc_norm|35.91|±  |  3.24|

Average: 47.2%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |59.22|±  |  1.44|
|             |       |acc_norm|61.43|±  |  1.42|
|arc_easy     |      0|acc     |84.26|±  |  0.75|
|             |       |acc_norm|83.63|±  |  0.76|
|boolq        |      1|acc     |88.69|±  |  0.55|
|hellaswag    |      0|acc     |65.98|±  |  0.47|
|             |       |acc_norm|84.29|±  |  0.36|
|openbookqa   |      0|acc     |34.20|±  |  2.12|
|             |       |acc_norm|47.20|±  |  2.23|
|piqa         |      0|acc     |81.83|±  |  0.90|
|             |       |acc_norm|82.59|±  |  0.88|
|winogrande   |      0|acc     |78.45|±  |  1.16|

Average: 75.18%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |48.47|±  |  1.75|
|             |       |mc2   |64.73|±  |  1.53|

Average: 64.73%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|61.05|±  |  3.55|
|bigbench_date_understanding                     |      0|multiple_choice_grade|68.56|±  |  2.42|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|35.27|±  |  2.98|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|31.20|±  |  2.45|
|                                                |       |exact_str_match      | 0.00|±  |  0.00|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|30.00|±  |  2.05|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|23.43|±  |  1.60|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|46.00|±  |  2.88|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|35.60|±  |  2.14|
|bigbench_navigate                               |      0|multiple_choice_grade|57.50|±  |  1.56|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|55.80|±  |  1.11|
|bigbench_ruin_names                             |      0|multiple_choice_grade|45.98|±  |  2.36|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|40.58|±  |  1.56|
|bigbench_snarks                                 |      0|multiple_choice_grade|66.85|±  |  3.51|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|71.40|±  |  1.44|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|56.40|±  |  1.57|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|24.00|±  |  1.21|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|17.09|±  |  0.90|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|46.00|±  |  2.88|

Average: 45.15%

Average score: 58.07%

Elapsed time: 04:05:27


### 📚 Citations 

```bibtex
@misc{kim2023solar,
      title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling}, 
      author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
      year={2023},
      eprint={2312.15166},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers"}
transformers safetensors mixtral text-generation conversational en arxiv:2312.15166 license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us