asingh15/qwen-arc-abs-gemini-partial-uniform-sft-1epoch-icmlpaper-0125
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Jan 26, 2026Architecture:Transformer Warm

The asingh15/qwen-arc-abs-gemini-partial-uniform-sft-1epoch-icmlpaper-0125 is a 4 billion parameter language model based on the Qwen architecture, featuring a 40960 token context length. This model is a fine-tuned variant, likely optimized for specific tasks related to its training methodology, as indicated by "partial-uniform-sft" and "icmlpaper-0125". Its primary differentiator and specific use cases are not detailed in the provided information, suggesting it may be an experimental or research-focused model.

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Model Overview

The asingh15/qwen-arc-abs-gemini-partial-uniform-sft-1epoch-icmlpaper-0125 is a 4 billion parameter language model built upon the Qwen architecture, supporting a substantial context length of 40960 tokens. The model's name suggests it has undergone a specific fine-tuning process, potentially involving "partial-uniform-sft" (Supervised Fine-Tuning) and is linked to research presented in an "icmlpaper-0125".

Key Characteristics

  • Architecture: Qwen-based language model.
  • Parameter Count: 4 billion parameters.
  • Context Length: Supports a large context window of 40960 tokens.
  • Training: Implies a specialized fine-tuning approach (partial-uniform-sft) and connection to an ICML paper, indicating a research-oriented development.

Current Status

As per the provided model card, specific details regarding its development, funding, language support, license, and direct use cases are currently marked as "More Information Needed". This suggests the model is either in an early stage of documentation or is intended for a very specific, perhaps research-internal, application where broader public details are not yet available.

Limitations

Due to the lack of detailed information in the model card, specific biases, risks, and limitations are not yet documented. Users are advised to be aware that without further details, the model's performance and suitability for various tasks cannot be fully assessed.