Adding Evaluation Results (#1)

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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)

	![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\|