Instructions to use MakiAi/qwen35-4b-codex-mobile-colab-t4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MakiAi/qwen35-4b-codex-mobile-colab-t4-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "MakiAi/qwen35-4b-codex-mobile-colab-t4-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Qwen3.5-4B Codex Mobile Colab T4 LoRA
Small experimental LoRA adapter trained with Unsloth on Google Colab T4 via Google Colab CLI.
Base model
unsloth/Qwen3.5-4B
Experiment
- Session:
unsloth-qwen35-4b-t4 - GPU observed:
Tesla T4, 15360 MiB, 14910 MiB - Training examples:
20 - Max sequence length:
1024 - Max steps:
20 - LoRA rank:
16 - Seed:
3407 - Output folder in Colab Drive:
/content/drive/MyDrive/colab-cli-unsloth-qwen35-4b
Intended behavior
Tiny Japanese experiment-report style adapter. It nudges answers toward concise conclusions, observations, next actions, Drive-mounted saves, evidence, and reproducibility.
See comparison.md and comparison.json for before/after response checks.
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