Instructions to use davidcondrey/llama-3.3-70B-i-ft-rev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use davidcondrey/llama-3.3-70B-i-ft-rev2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") model = PeftModel.from_pretrained(base_model, "davidcondrey/llama-3.3-70B-i-ft-rev2") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Llama-3-70B Literary Fine-tune v2
Fine-tuned on literary analysis and creative writing.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Meta-Llama-3-70B",
load_in_4bit=True,
device_map="auto"
)
# Load tokenizer and adapter
tokenizer = AutoTokenizer.from_pretrained("davidcondrey/llama3-70b-lit-rev2")
model = PeftModel.from_pretrained(base_model, "davidcondrey/llama3-70b-lit-rev2")
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Base model
meta-llama/Meta-Llama-3-70B