unconst/Affine-5czsc2fc98-r502-sbsv5-offline-dpo-hialpha-hirank-lobeta-midctx-extrasteps-merged

TEXT GENERATIONPricing:Input $0.4 / Cached $0.07 / Output $4Concurrent Unit Cost:3Model Size:35.1BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 16, 2026Architecture:Transformer Featherless Exclusive Cold

The unconst/Affine-5czsc2fc98-r502-sbsv5-offline-dpo-hialpha-hirank-lobeta-midctx-extrasteps-merged model is a 35.1 billion parameter language model, merged from a LoRA fine-tuned version of kevin954/Affine-5dfqbbh8ev-sft. With a context length of 32768 tokens, this model is noted as a private TTL insurance checkpoint, indicating its development for specific, potentially internal, applications. Its primary differentiator lies in its specialized fine-tuning, suggesting optimization for particular domain tasks rather than general-purpose use.

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Model Overview

The unconst/Affine-5czsc2fc98-r502-sbsv5-offline-dpo-hialpha-hirank-lobeta-midctx-extrasteps-merged is a 35.1 billion parameter language model with a substantial context length of 32768 tokens. This model is a result of a LoRA merge operation, originating from the kevin954/Affine-5dfqbbh8ev-sft base model.

Key Characteristics

  • Parameter Count: 35.1 billion parameters, indicating a large-scale model capable of complex language understanding and generation.
  • Context Length: Supports a context window of 32768 tokens, allowing for processing and generating longer sequences of text.
  • Origin: Merged from a LoRA fine-tuned checkpoint, suggesting specialized training beyond its base model.
  • Development Status: Described as a "private TTL insurance" checkpoint, implying it's an internal or domain-specific development, not yet a public submission.

Potential Use Cases

Given its specialized fine-tuning and private development status, this model is likely intended for:

  • Domain-Specific Applications: Optimized for particular tasks or datasets relevant to its "TTL insurance" context.
  • Research and Development: Suitable for further experimentation and integration into specific systems where its fine-tuned characteristics are beneficial.
  • Internal Systems: Designed for use within a controlled environment, leveraging its unique training for targeted performance.

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