Instructions to use mohith-das/jetson-flat-cache-cpt-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mohith-das/jetson-flat-cache-cpt-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("../01_distillation_assistant/final_jetson_assistant_0.5b") model = PeftModel.from_pretrained(base_model, "mohith-das/jetson-flat-cache-cpt-adapter") - Notebooks
- Google Colab
- Kaggle
jetson-flat-cache-cpt-adapter
LoRA adapter from the Continual Pre-Training (CPT) stabilization pass in Phase 2 of the llm_distillery project. Trained on top of a Qwen2.5-0.5B model whose attention layers were surgically remapped to an alternating Sliding-Window Attention (SWA) / Gated Linear Attention (GLA) hybrid, to stabilize the newly-introduced GLA decay parameters and let the model adapt to its new attention mechanism.
- Base model (pre-surgery):
mohith-das/jetson-assistant-0.5b - 200 steps, LoRA r=8/alpha=16 on
q_proj,k_proj,v_proj,o_proj, plus the GLA layers'log_decayparameters unfrozen - Trained on Jetson Orin Nano Super (JetPack 7.2, CUDA 13.2)
See the merged full-precision model at
mohith-das/jetson-hybrid-flat-cache-0.5b for the ready-to-use checkpoint
with this adapter already folded in.
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