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

python

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

duoyuncloud/zsh-autocomplete-lora

zsh-autocomplete-lora

Model Details

LoRA Configuration