Text Generation
PEFT
Safetensors
English
lora
syllogistic-reasoning
semeval2026
task11
logical-reasoning
conversational
Instructions to use maytemuma/llamax3-8b-lora-fewshot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use maytemuma/llamax3-8b-lora-fewshot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LLaMAX/LLaMAX3-8B") model = PeftModel.from_pretrained(base_model, "maytemuma/llamax3-8b-lora-fewshot") - Notebooks
- Google Colab
- Kaggle
Download split_info.json from maytemuma/llamax3-8b-lora-fewshot: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/maytemuma/llamax3-8b-lora-fewshot/resolve/main/split_info.json
- Command line
-
hf download hf://maytemuma/llamax3-8b-lora-fewshot/split_info.json
-
curl -L -o split_info.json https://huggingface.co/maytemuma/llamax3-8b-lora-fewshot/resolve/main/split_info.json
190 Bytes
| { | |
| "train_size": 648, | |
| "dev_size": 72, | |
| "train_valid": 324, | |
| "train_invalid": 324, | |
| "dev_valid": 36, | |
| "dev_invalid": 36, | |
| "seed": 42, | |
| "dev_ratio": 0.1, | |
| "prompt_type": "few-shot" | |
| } |