DCAgent/a1-stack_bash
DCAgent/a1-stack_bash is an 8 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen3-8B. This model is specifically optimized for tasks related to bash scripting, leveraging a dataset derived from `exp_rpt_stack-bash-withtests_glm_4.7_traces_jupiter`. It is designed to assist with bash-related queries and code generation, offering a specialized solution for developers working with shell environments. The model has a context length of 32768 tokens, supporting extensive bash script analysis and generation.
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Overview
DCAgent/a1-stack_bash is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. This model has been specialized through training on the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_stack-bash-withtests_glm_4.7_traces_jupiter dataset, which focuses on bash scripting with tests. It is designed to understand and generate bash commands and scripts, making it a specialized tool for shell-related tasks.
Key Capabilities
- Bash Scripting: Optimized for generating and understanding bash commands and scripts.
- Context Handling: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer bash sequences.
- Fine-tuned Performance: Leverages the base capabilities of Qwen3-8B, enhanced for specific bash-related applications.
Training Details
The model was trained with a learning rate of 4e-05, a total batch size of 16 across 16 devices, and for 7 epochs. It utilized an AdamW optimizer with a cosine learning rate scheduler and a warmup ratio of 0.1. The training was conducted using Transformers 4.57.6 and Pytorch 2.9.1+cu130.
Intended Use Cases
- Bash Code Generation: Generating bash scripts for various automation or system administration tasks.
- Script Analysis: Assisting in understanding or debugging existing bash scripts.
- Developer Tooling: Integrating into development workflows that require bash command assistance.