nbeerbower/Mistral-Nemo-Prism-12B-v7

Hugging Face
TEXT GENERATIONPricing:Input $0.87 / Cached $0.2 / Output $0.99Concurrent Unit Cost:1Model Size:12BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Nov 13, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Warm

nbeerbower/Mistral-Nemo-Prism-12B-v7 is a 12 billion parameter experimental language model developed by nbeerbower, based on the Mahou-1.5-mistral-nemo-12B-lorablated architecture. It was fine-tuned using ORPO on the Arkhaios-DPO and Purpura-DPO datasets with a 32768 token context length. The primary goal of this model is to reduce archaic language and purple prose while remaining uncensored, though the developer notes that LoRA fine-tuning did not fully resolve these language issues.

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Mistral-Nemo-Prism-12B-v7 Overview

This model, developed by nbeerbower, is an experimental 12 billion parameter language model built upon the Mahou-1.5-mistral-nemo-12B-lorablated base. It was fine-tuned using the ORPO method over 10 epochs on the Arkhaios-DPO and Purpura-DPO datasets, with an increased beta value of 2 for this specific version. The model supports a context length of 32768 tokens.

Key Capabilities

  • Uncensored Output: Designed to be completely uncensored.
  • Language Style Experimentation: Specifically aimed at reducing archaic language and "purple prose" in its generations.

Good For

  • Experimental Use: Ideal for researchers and developers interested in exploring the effects of ORPO fine-tuning on language style and censorship.
  • Understanding Fine-tuning Limitations: Useful for observing the challenges in completely altering deeply embedded language characteristics through LoRA fine-tuning.

Note: This is an experimental iteration and not intended for production-ready applications, as the developer indicates that some language issues persist despite the fine-tuning efforts.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
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top_p
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top_k
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frequency_penalty
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presence_penalty
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repetition_penalty
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min_p
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