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
File size: 464 Bytes
143f056 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # 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
|