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MrHungLe01
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Model Details
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MrHungLe01
Model Tree
MrHungLe01/SmolLM2-135M-Instruction-Tuned
Input Modalities
Output Modalities
Supported Functionality
This repository contains the LoRA (Low-Rank Adaptation) adapter weights fine-tuned on top of MrHungLe01/SmolLM2-135M-Instruction-Tuned.
This project is part of Step 6: Deep Dive into LoRA, focusing on Parameter-Efficient Fine-Tuning (PEFT) using matrix decomposition to reduce memory footprint while maintaining downstream performance.
This adapter is intended for mentor evaluation and testing text generation tasks. It requires the base model to be loaded concurrently.
This model is a lightweight experiment and should not be deployed in production environments or safety-critical applications.
You can load and evaluate this adapter using the following snippet:
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_model_id = "MrHungLe01/SmolLM2-135M-Instruction-Tuned"adapter_id = "MrHungLe01/SmolLM2-135M-LoRA-Adapter" # Update with your exact repo name# 1. Load Base Modelmodel = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")tokenizer = AutoTokenizer.from_pretrained(base_model_id)# 2. Load and Apply LoRA Weightsmodel = PeftModel.from_pretrained(model, adapter_id)# 3. Quick Inference Testinputs = tokenizer("Explain the concept of LoRA in one sentence:", return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=50)print(tokenizer.decode(outputs[0], skip_special_tokens=True))