elevateecho/sn120-a871a29bd76e

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 elevateecho/sn120-a871a29bd76e model is a 35.1 billion parameter language model, originating from a LoRA-merged checkpoint of kevin954/Affine-5dfqbbh8ev-sft. With a context length of 32768 tokens, this model is currently under private development for TTL insurance applications. Its primary characteristic is its foundation as a salvaged and merged checkpoint, indicating a focus on refining or adapting an existing architecture for specific, potentially niche, enterprise use cases.

Loading preview...

Model Overview

The elevateecho/sn120-a871a29bd76e is a substantial 35.1 billion parameter language model with a context length of 32768 tokens. It is derived from a LoRA-merged checkpoint of kevin954/Affine-5dfqbbh8ev-sft. This model is currently in a private development phase, specifically noted for "TTL insurance" applications, and is not yet considered a public submission.

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: Developed from a LoRA-merged checkpoint, suggesting fine-tuning or adaptation of a base model for specialized performance.
  • Development Status: Currently under private development for specific enterprise applications (TTL insurance) and is not yet publicly released or validated through a "Stage-5 gate."

Potential Use Cases

Given its private development for "TTL insurance," this model is likely being optimized for:

  • Specialized Domain Tasks: Processing and generating content relevant to the insurance industry, potentially including policy analysis, claim processing, or customer service automation.
  • Large-Scale Text Analysis: Leveraging its substantial parameter count and context length for in-depth analysis of complex insurance documents or large datasets.
  • Enterprise Applications: Designed for integration into specific business workflows where domain-specific accuracy and performance are critical.

Finetunes
1 models