zsh-autocomplete-lora

LoRA adapter for local Zsh CLI command completion, used by the duoyuncloud/zsh-llm-cli-autocomplete-tool daemon runtime.

Model Details

• Adapter type: PEFT LoRA (task_type=CAUSAL_LM)
• Base model: Qwen/Qwen2.5-0.5B-Instruct
• Primary use case: inline shell command completion in Zsh
• Language: shell command text (plus lightweight natural language in prompts)

LoRA Configuration

From adapter_config.json:

• r: 16
• lora_alpha: 32
• lora_dropout: 0.05
• target_modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• inference_mode: true

Intended Use

• Low-latency local command completion for terminal workflows
• Context-aware completion with shell history and lightweight repository context from host app
• Smart commit command suggestion (handled by host app logic + this adapter)

Out-of-Scope Use

• Safety-critical automation without user review
• General-purpose factual Q&A or long-form generation
• Execution of generated commands without human confirmation

Quick Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "duoyuncloud/zsh-autocomplete-lora"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(base_id, trust_remote_code=True)
model = PeftModel.from_pretrained(base, adapter_id)

# Optional: merge for faster standalone inference
merged = model.merge_and_unload()

For this project, installation and daemon wiring are handled by:

• install.sh in the project repository
• python -m model_completer.daemon runtime entrypoint

Training Notes

This adapter is trained with SFT-style shell completion data (instruction / input / output) and optimized for local interactive inference.

Limitations

• May suggest incorrect or unsafe commands in ambiguous contexts
• Performance depends on user history/context quality from host integration
• Should be used with explicit user acceptance before command execution

Safety

• Keep human-in-the-loop confirmation for command execution
• Apply allow/deny checks in host app for risky flags/operations
• Avoid using generated output as direct automation input

Links

• Project repository: https://github.com/duoyuncloud/zsh-llm-cli-autocomplete-tool
• Runtime install script and daemon integration are maintained in the project repository above.