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---
language:
- en
license: apache-2.0
library_name: transformers
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)
## 🌅 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|
|