Adding Evaluation Results (#5)

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- open-source
	- code
	- math
	- chemistry
	- biology
	- text-generation
	- question-answering
	datasets:
	- Locutusque/OpenCerebrum-dpo
	pipeline_tag: text-generation
	model-index:
	- name: OpenCerebrum-1.0-7b-DPO
	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: 62.71
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
	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: 84.33
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
	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: 62.59
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
	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: 44.91
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
	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: 80.11
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
	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: 42.0
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
	name: Open LLM Leaderboard
	---

	# OpenCerebrum-1.0-7B-DPO

	OpenCerebrum-1.0-7B-DPO is an open-source language model fine-tuned from the alpindale/Mistral-7B-v0.2-hf base model on a diverse dataset aimed at replicating capabilities of Aether Research's proprietary Cerebrum model.

	The model was fine-tuned on approximately 21,000 examples across 6 datasets spanning coding, math, science, reasoning, and general instruction-following. The goal was to assemble public datasets that could help the model achieve strong performance on benchmarks where Cerebrum excels.

	I used the ChatML prompt format to train this model.

	## Model Details

	- Base Model: alpindale/Mistral-7B-v0.2-hf
	- Parameters: 7 billion
	- Fine-Tuning Dataset Size: ~21,000 examples
	- Fine-Tuning Data: Amalgamation of 6 public datasets
	- Language: English
	- License: Apache 2.0

	## Quants

	- ExLlamaV2: https://huggingface.co/bartowski/OpenCerebrum-1.0-7b-DPO-exl2
	- GGUF: https://huggingface.co/bartowski/OpenCerebrum-1.0-7b-DPO-GGUF
	- AWQ: https://huggingface.co/solidrust/OpenCerebrum-1.0-7b-DPO-AWQ

	## Intended Use

	OpenCerebrum-1.0-7B-DPO is intended to be a powerful open-source model for coding, math, science, and general question-answering and text generation tasks. Its diverse fine-tuning data aims to equip it with broad knowledge and reasoning capabilities.

	However, as an open-source replica trained on a subset of data compared to the original Cerebrum, it may not match Cerebrum's full performance. Additionally, biases and limitations of the fine-tuning data may be reflected in the model's outputs.

	## Limitations and Biases

	- The model may have biases and limitations inherited from its fine-tuning datasets. Thorough testing is needed to characterize these.
	- With 21,000 training examples, the fine-tuning data is still limited compared to the proprietary Cerebrum data.
	- As the model is based on a 7B parameter model, it has computational and memory constraints compared to larger models.

	## Training Details

	The model was fine-tuned on the 6 datasets listed in the Datasets section, totaling approximately 21,000 examples. In the future, the fine-tuning dataset may be condensed to more closely match the ~500 example dataset reputedly used for the original Cerebrum model.
	# [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_Locutusque__OpenCerebrum-1.0-7b-DPO)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|62.78\|
	\|AI2 Reasoning Challenge (25-Shot)\|62.71\|
	\|HellaSwag (10-Shot) \|84.33\|
	\|MMLU (5-Shot) \|62.59\|
	\|TruthfulQA (0-shot) \|44.91\|
	\|Winogrande (5-shot) \|80.11\|
	\|GSM8k (5-shot) \|42.00\|