ccui46/hazardworld_per_chunk_act_q3_tokfix_diffPrompt_higherLR_tformerPin_3000

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Apr 19, 2026Architecture:Transformer Cold

The ccui46/hazardworld_per_chunk_act_q3_tokfix_diffPrompt_higherLR_tformerPin_3000 is an 8 billion parameter language model with a 32768 token context length. This model is a Hugging Face transformer model, though specific architectural details and its primary developer are not provided in the available information. Its unique characteristics and primary use cases are not detailed in the current model card, indicating a need for more information regarding its specific optimizations or applications.

Loading preview...

Overview

This model, ccui46/hazardworld_per_chunk_act_q3_tokfix_diffPrompt_higherLR_tformerPin_3000, is an 8 billion parameter language model hosted on Hugging Face. It features a substantial context length of 32768 tokens, suggesting potential for processing long sequences of text. The model card indicates it is a 🤗 transformers model, but detailed information regarding its specific architecture, development team, training data, or fine-tuning objectives is currently marked as "More Information Needed."

Key Characteristics

  • Parameter Count: 8 billion parameters.
  • Context Length: 32768 tokens, allowing for extensive input and output sequences.
  • Model Type: Hugging Face transformers model.

Current Limitations and Information Gaps

As per the provided model card, significant details are pending, including:

  • The specific developer or organization behind the model.
  • The model's architecture and language(s) it supports.
  • Its intended direct and downstream use cases.
  • Details on training data, procedures, and evaluation results.
  • Information regarding biases, risks, and environmental impact.

Users should be aware that without further details, the specific strengths, weaknesses, and optimal applications of this model remain undefined. More information is needed to assess its suitability for particular use cases or to compare it effectively with other models.