unconst/Affine-5czsc2fc98-r483-offline-dpo-hialpha-hibeta-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-r483-offline-dpo-hialpha-hibeta-extrasteps-merged model is a 35.1 billion parameter language model, merged from a LoRA of kevin954/Affine-5dfqbbh8ev-sft. With a context length of 32768 tokens, this model is a salvaged checkpoint, indicating its development as part of an iterative refinement process. Its primary differentiator lies in its origin as a merged checkpoint, suggesting a focus on combining or refining specific capabilities from its base model.

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Model Overview

The unconst/Affine-5czsc2fc98-r483-offline-dpo-hialpha-hibeta-extrasteps-merged model is a substantial 35.1 billion parameter language model, featuring a context window of 32768 tokens. This model represents a merged checkpoint, specifically salvaged from a LoRA (Low-Rank Adaptation) of 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 tasks.
  • Context Length: Supports a significant context window of 32768 tokens, allowing it to process and generate longer sequences of text while maintaining coherence.
  • Origin: Derived from a LoRA merge of kevin954/Affine-5dfqbbh8ev-sft, suggesting it incorporates fine-tuned adaptations or specialized knowledge from its predecessor.
  • Development Status: Described as a "merged checkpoint salvage," implying it is an intermediate or refined version from an ongoing development process, potentially aimed at improving specific aspects of the base model.

Potential Use Cases

Given its large parameter count and substantial context window, this model could be suitable for applications requiring:

  • Advanced text generation and completion.
  • Complex reasoning and understanding of long documents.
  • Tasks benefiting from a model that has undergone specific fine-tuning or merging processes, potentially for domain-specific applications or improved performance in certain areas.

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