transformers safetensors mixtral text-generation conversational en arxiv:2312.15166 license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: apache-2.0
model-index:
- name: SOLAR-math-2x10.7b
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 68.43
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 86.31
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 66.9
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 64.21
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 83.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 71.04
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b
      name: Open LLM Leaderboard
---

# 🌞🚀 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}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__SOLAR-math-2x10.7b)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |73.37|
|AI2 Reasoning Challenge (25-Shot)|68.43|
|HellaSwag (10-Shot)              |86.31|
|MMLU (5-Shot)                    |66.90|
|TruthfulQA (0-shot)              |64.21|
|Winogrande (5-shot)              |83.35|
|GSM8k (5-shot)                   |71.04|

