Locutusque
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OpenCerebrum-1.0-7b-SFT

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 ---
+language:
+- en
+license: apache-2.0
+tags:
+- open-source
+- code
+- math
+- chemistry
+- biology
+- question-answering
+- text-generation
+datasets:
+- Open-Orca/SlimOrca
+- glaiveai/glaive-code-assistant
+- camel-ai/physics
+- camel-ai/math
+- camel-ai/chemistry
+- camel-ai/biology
+- WizardLM/WizardLM_evol_instruct_V2_196k
+- microsoft/orca-math-word-problems-200k
+- grimulkan/theory-of-mind
+- Vezora/Tested-22k-Python-Alpaca
+- m-a-p/Code-Feedback
+- Locutusque/arc-cot
+- jondurbin/airoboros-2.1
+- WizardLM/WizardLM_evol_instruct_70k
 ---
+# OpenCerebrum-1.0-7B-SFT
+OpenCerebrum-1.0-7B-SFT 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 Anthropic's proprietary Cerebrum model.
+The model was fine-tuned on approximately 1.2 million examples across 14 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.
 ## Model Details
+- **Base Model:** alpindale/Mistral-7B-v0.2-hf
+- **Parameters:** 7 billion
+- **Fine-Tuning Dataset Size:** ~1,200,000 examples
+- **Fine-Tuning Data:** Amalgamation of 14 public datasets
+- **Language:** English
+- **License:** Apache 2.0
+## Intended Use
+OpenCerebrum-1.0-7B-SFT 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 1.2 million 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 14 datasets listed in the Datasets section, totaling approximately 1.2 million examples. Default training hyperparameters were used. In the future, the fine-tuning dataset may be condensed to more closely match the 5,000 example dataset reputedly used for the original Cerebrum model.