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# Download Lemonade from https://lemonade-server.ai/
lemonade pull burhan2753y/qwen2.5-coder-0.5b-nl2bash-gguf
Run and chat with the model
lemonade run user.qwen2.5-coder-0.5b-nl2bash-gguf-{{QUANT_TAG}}
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🚀 Qwen2.5-Coder-0.5B-Instruct - NL2Bash (GGUF Quantized)

A fine-tuned version of Qwen/Qwen2.5-Coder-0.5B-Instruct specialized for converting natural language instructions directly into executable Linux/Ubuntu Bash commands with zero conversational filler.

📊 Dual-Track Benchmark Results

🎓 Track 1: Academic NL2Bash Benchmark (EMNLP 2018 / Microsoft CodeXGLUE)

Metric Base Model (qwen2.5-coder:0.5b) Fine-Tuned Model (bash-coder-assistant) Improvement
Exact Match (EM %) 0.0% 44.0% +44.0%
Token F1 Score 12.93 81.60 +68.7 pts
BLEU-4 Score 4.60 65.01 +60.4 pts
Bash Syntax Validity 88.0% 100.0% +12.0%
Zero-Chatter Clean Format 4.0% 100.0% +96.0%
Mean Latency (CPU) 19.03s 1.58s ⚡ 91.7% Faster

🛠️ Track 2: Real-World DevOps, Cloud & SysAdmin Benchmark

Metric Base Model (qwen2.5-coder:0.5b) Fine-Tuned Model (bash-coder-assistant) Improvement
Exact Match (EM %) 0.0% 15.0% +15.0%
Token F1 Score 6.76 62.42 +55.7 pts
Bash Syntax Validity 90.0% 100.0% +10.0%
Mean Latency (CPU) 20.98s 1.78s ⚡ 91.5% Faster

🦙 Quickstart with Ollama

  1. Download the qwen2.5-coder-0.5b-bash-gguf.gguf file and Modelfile.
  2. Create the model in Ollama:
ollama create bash-coder-assistant -f Modelfile
  1. Run a query:
ollama run bash-coder-assistant "Find all processes running on port 8080 and terminate them immediately"
# Output: sudo kill -9 $(lsof -i :8080 | awk '{print $2}')

🛠️ Training Details

  • Architecture: Qwen2.5-Coder (0.5B parameters)
  • Framework: Unsloth + TRL SFTTrainer
  • Dataset: emirkaanozdemr/bash_command_data_6K
  • LoRA Rank: r=16, lora_alpha=16
  • Training Compute: Google Colab Tesla T4 GPU (< 2GB VRAM, ~8 min runtime)
  • Final Loss: 0.6726 (converged from initial 4.1045)
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