Instructions to use bziemba/ultra-lora-3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bziemba/ultra-lora-3000 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B-Base") model = PeftModel.from_pretrained(base_model, "bziemba/ultra-lora-3000") - Notebooks
- Google Colab
- Kaggle
| # ollama modelfile auto-generated by llamafactory | |
| FROM . | |
| TEMPLATE """{{ if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user | |
| {{ .Content }}<|im_end|> | |
| <|im_start|>assistant | |
| {{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|> | |
| {{ end }}{{ end }}""" | |
| SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant.""" | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER num_ctx 4096 | |