Text Generation
PEFT
Safetensors
smollm3
lora
context-compression
agent-memory
membrane
conversational
Instructions to use homerquan/mn-context-engine-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use homerquan/mn-context-engine-lora-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B") model = PeftModel.from_pretrained(base_model, "homerquan/mn-context-engine-lora-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bda66be06e527bb3fb49cd09145bd065813ab384b7560d28c248643ff67c841
- Size of remote file:
- 17.2 MB
- SHA256:
- a10506ce184da0c0de8dd71c31252bf4cc6569b198ba30ea8ce5a57dc518387e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.