carlosqsw/longpt_trace_qwen3_4b_instruct_sft_b128

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 8, 2026Architecture:Transformer Featherless Exclusive Cold

The carlosqsw/longpt_trace_qwen3_4b_instruct_sft_b128 is a 4 billion parameter instruction-tuned language model. This model is based on the Qwen3 architecture and has been fine-tuned for specific instruction-following tasks. It features a notable context length of 32768 tokens, making it suitable for processing extensive inputs. The model's primary strength lies in its ability to handle long-context interactions and follow complex instructions effectively.

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Model Overview

This model, carlosqsw/longpt_trace_qwen3_4b_instruct_sft_b128, is a 4 billion parameter instruction-tuned language model built upon the Qwen3 architecture. It has been specifically fine-tuned to excel in instruction-following tasks, making it a robust choice for applications requiring precise responses to user prompts.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Features an extended context window of 32768 tokens, enabling it to process and understand significantly longer inputs and maintain coherence over extended conversations or documents.
  • Instruction Following: Optimized through supervised fine-tuning (SFT) to accurately interpret and execute a wide range of instructions.

Potential Use Cases

Given its instruction-following capabilities and large context window, this model is well-suited for:

  • Long-form content generation: Creating detailed articles, reports, or creative writing pieces that require maintaining context over many paragraphs.
  • Complex query answering: Responding to intricate questions that necessitate understanding multiple pieces of information from a lengthy prompt.
  • Code analysis and generation: Potentially assisting with code-related tasks where understanding large codebases or detailed specifications is crucial.
  • Conversational AI: Developing chatbots or virtual assistants that can handle extended dialogues and remember past interactions within the conversation.

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