ichigoberry/pandafish-7b
ichigoberry/pandafish-7b is a 7 billion parameter instruct model based on a Model Stock merge of Mistral-7B-v0.1, Mistral-7B-Instruct-v0.2, CultriX/NeuralTrix-bf16, and OpenPipe/mistral-ft-optimized-1227. This model is designed for general instruction-following tasks, leveraging its merged architecture to provide a balanced performance across various benchmarks. It offers an 8192 token context length, making it suitable for applications requiring moderate input and output lengths.
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Overview
pandafish-7b is a 7 billion parameter instruction-tuned model created by ichigoberry. It is built using a "Model Stock" merge method, combining several Mistral-based models: mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.2, CultriX/NeuralTrix-bf16, and OpenPipe/mistral-ft-optimized-1227. This merging strategy aims to synthesize the strengths of its constituent models into a single, capable instruction-following LLM.
Key Capabilities & Performance
The model demonstrates competitive performance across various evaluation benchmarks. While its overall average score is slightly below Mistral-7B-Instruct-v0.2, pandafish-7b shows notable strengths in specific areas:
- AGIEval: Achieves a score of 40, outperforming
Mistral-7B-Instruct-v0.2(38.5). - GPT4All: Scores 74.23, also surpassing
Mistral-7B-Instruct-v0.2(71.64).
These results suggest that pandafish-7b may excel in tasks related to general knowledge and reasoning, as indicated by its higher scores on AGIEval and GPT4All. The model operates with a context length of 8192 tokens.
Usage
Developers can integrate pandafish-7b using the Hugging Face transformers library, with support for bfloat16 precision and device_map="auto" for efficient deployment.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.