carlosqsw/longpt_trace_qwen3_4b_sft_04

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 12, 2026Architecture:Transformer Featherless Exclusive Cold

The carlosqsw/longpt_trace_qwen3_4b_sft_04 is a 4 billion parameter language model developed by carlosqsw, featuring a notable context length of 32768 tokens. This model is fine-tuned, indicating optimization for specific tasks or improved performance over its base architecture. Its substantial context window suggests suitability for applications requiring extensive input understanding or generation, such as long-form content creation or complex document analysis.

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Model Overview

The carlosqsw/longpt_trace_qwen3_4b_sft_04 is a 4 billion parameter language model, distinguished by its extended context length of 32768 tokens. This model has undergone supervised fine-tuning (SFT), which typically enhances its ability to follow instructions and perform specific tasks more effectively than a base model.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: A significant 32768-token context window, enabling the processing and generation of very long sequences of text.
  • Fine-tuned: The model is fine-tuned, suggesting it has been optimized for particular applications or improved general performance.

Potential Use Cases

Given its substantial context length and fine-tuned nature, this model is likely well-suited for applications that benefit from processing extensive textual information.

  • Long-form content generation: Creating detailed articles, reports, or creative writing pieces.
  • Complex document analysis: Summarizing, extracting information, or answering questions from large documents.
  • Conversational AI: Maintaining coherent and contextually relevant dialogue over extended interactions.
  • Code generation and analysis: Handling larger codebases or complex programming tasks where context is crucial.