unconst/Affine-5czsc2fc98-r560-r252-odpo-hirank-longctx-extra-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-r560-r252-odpo-hirank-longctx-extra-merged model is a 35.1 billion parameter language model, derived from a LoRA merge of `kevin954/Affine-5dfqbbh8ev-sft`. This model is noted as a merged checkpoint salvage, intended for private TTL insurance purposes. With a substantial 32768 token context length, it is designed for applications requiring extensive contextual understanding.

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Model Overview

The unconst/Affine-5czsc2fc98-r560-r252-odpo-hirank-longctx-extra-merged model is a large language model with 35.1 billion parameters and an impressive 32768 token context length. It is a result of a LoRA merge operation, specifically combining elements from kevin954/Affine-5dfqbbh8ev-sft.

Key Characteristics

  • Parameter Count: 35.1 billion, indicating a robust capacity for complex language tasks.
  • Context Length: 32768 tokens, enabling the processing and generation of very long sequences of text, crucial for applications requiring deep contextual understanding.
  • Origin: This model is described as a "merged checkpoint salvage," suggesting it incorporates refined or recovered training states.
  • Development Status: It is noted for "Private TTL insurance" and is not yet a public submission, indicating it is in an internal or pre-release phase of development.

Potential Use Cases

Given its substantial parameter count and extended context window, this model could be particularly well-suited for:

  • Long-form content generation: Creating extensive articles, reports, or creative narratives.
  • Complex document analysis: Summarizing, extracting information, or answering questions from very large texts.
  • Conversational AI: Maintaining coherent and contextually relevant dialogues over many turns.
  • Code generation and analysis: Handling large codebases or complex programming tasks that benefit from a broad contextual view.