Instructions to use mohith-das/jetson-assistant-stage2-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mohith-das/jetson-assistant-stage2-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B") model = PeftModel.from_pretrained(base_model, "mohith-das/jetson-assistant-stage2-adapter") - Notebooks
- Google Colab
- Kaggle
jetson-assistant-stage2-adapter
Stage 2 ("personal alignment") LoRA adapter from the llm_distillery project.
Trained on top of Stage 1's distilled knowledge
(jetson-assistant-stage1-adapter
merged into the base, then wrapped in a fresh LoRA โ not stacked as a second
adapter) on a small handwritten dataset (~60 examples) of personal-assistant
behaviors: message drafting, calendar/scheduling, contact lookup,
group-chat summarizing, and daily-workflow utilities.
Training: LoRA r=16/alpha=32 on all linear projections (q/k/v/o/gate/up/down proj), lr=5e-5, 2 epochs (~30 optimizer steps), max_length=512, FP16. Deliberately gentle settings โ the dataset is tiny, and the goal is to layer on assistant behavior without disturbing Stage 1's reasoning/coding gains.
Note: this adapter's base_model_name_or_path metadata points at the
plain Qwen2.5-0.5B base (a PEFT config quirk), but it was actually trained
against Stage 1's merged weights. Apply it on top of Stage 1 merged in, or
just use the final combined checkpoint at
jetson-assistant-0.5b
directly.
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Model tree for mohith-das/jetson-assistant-stage2-adapter
Base model
Qwen/Qwen2.5-0.5B