laion/swesmith-nl2bash-stack-bugsseq

Hugging Face
TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Dec 17, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Warm

The laion/swesmith-nl2bash-stack-bugsseq model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It specializes in tasks related to natural language to bash command translation, code bug identification, and general programming queries. This model leverages diverse datasets including those focused on nl2bash, Stack Overflow content, and inferred code bugs, making it particularly adept at understanding and generating code-related text within a 32,768 token context window.

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Model Overview

laion/swesmith-nl2bash-stack-bugsseq is an 8 billion parameter language model, fine-tuned from the robust Qwen/Qwen3-8B architecture. This model has been specifically adapted through training on a combination of specialized datasets, enhancing its capabilities in several key areas related to programming and natural language processing for code.

Key Capabilities

  • Natural Language to Bash (nl2bash): Optimized for translating natural language instructions into executable bash commands.
  • Code Bug Identification: Trained on datasets containing inferred code bugs, suggesting proficiency in recognizing and potentially assisting with debugging scenarios.
  • Stack Exchange & Programming Queries: Benefits from fine-tuning on Stack Overflow and similar content, making it suitable for understanding and generating responses to technical programming questions.

Training Details

The model was trained with a learning rate of 4e-05 over 7 epochs, utilizing a total batch size of 16 across 8 GPUs. The training incorporated a cosine learning rate scheduler with a 0.1 warmup ratio, using the AdamW_TORCH_FUSED optimizer. This rigorous training regimen on diverse, code-centric datasets aims to provide a specialized tool for developers and researchers working with code-related language tasks.

Good For

  • Developers needing to convert natural language requests into bash scripts.
  • Assisting with the identification or understanding of common code bugs.
  • Processing and generating content related to programming questions and answers, particularly from Stack Exchange-like sources.

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