GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
Rupa421
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
Rupa421
Model Tree
TinyLlama/TinyLlama-1.1B-Chat-v1.0
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModeltokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")model = PeftModel.from_pretrained(base_model,"Rupa421/TinyLlama-LoRA")
text
### Instruction:Explain what LoRA is.### Response:
This project was built to demonstrate an end-to-end parameter-efficient fine-tuning (PEFT) workflow using:
The project covers dataset formatting, prompt engineering, LoRA configuration, supervised fine-tuning, inference, and publishing the trained adapter to Hugging Face.
Built with ❤️ while learning PyTorch Internals and Transformer Architecture.