Text Generation
PEFT
Safetensors
English
lora
qlora
qwen2.5
finance
earnings-calls
information-extraction
structured-output
unsloth
conversational
Instructions to use BABAKAFSHINPOUR/earnings-call-guidance-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BABAKAFSHINPOUR/earnings-call-guidance-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-7B-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "BABAKAFSHINPOUR/earnings-call-guidance-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
- Xet hash:
- fb0d31f4a3bee488f0d36f4031c78bf07dfa11a835ed59b500f7279ee4605a2f
- Size of remote file:
- 11.4 MB
- SHA256:
- 637b0b967d8515cc5ee70d55859553251a205028c93d0f7c1b1ce0d2bf7a6a1a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.