Locutusque_-_OpenCerebrum-2.0-7B-8bits

Quantization made by Richard Erkhov.

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OpenCerebrum-2.0-7B - bnb 8bits

• Model creator: https://huggingface.co/Locutusque/
• Original model: https://huggingface.co/Locutusque/OpenCerebrum-2.0-7B/

Original model description:

language:

• en license: apache-2.0 tags:
• open-source
• code
• math
• chemistry
• biology
• text-generation
• question-answering pipeline_tag: text-generation

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OpenCerebrum-2.0-7B

OpenCerebrum-2.0-7B 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 with SFT and DPO on approximately 7,000 examples across 15 data sources spanning coding, math, science, multi-turn conversation, RAG, 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.

Model Details

• Base Model: alpindale/Mistral-7B-v0.2-hf
• Parameters: 7 billion
• Fine-Tuning Dataset Size: ~7,000 examples
• Fine-Tuning Data: Advanced in-house curation techniques at Cognitive Computations, with 15 different data sources for DPO and SFT.
• Language: English
• License: Apache 2.0

Quants

EXL2 @bartowski

• https://huggingface.co/bartowski/OpenCerebrum-2.0-7B-exl2

GGUF @bartowski

• https://huggingface.co/bartowski/OpenCerebrum-2.0-7B-GGUF

Intended Use

OpenCerebrum-2.0-7B 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.
• As the model is based on a 7B parameter model, it has computational and memory constraints compared to larger models.

Evaluations