Instructions to use fay-ong/new-llamafactory-llama-3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fay-ong/new-llamafactory-llama-3-8b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "fay-ong/new-llamafactory-llama-3-8b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use fay-ong/new-llamafactory-llama-3-8b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for fay-ong/new-llamafactory-llama-3-8b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for fay-ong/new-llamafactory-llama-3-8b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fay-ong/new-llamafactory-llama-3-8b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="fay-ong/new-llamafactory-llama-3-8b", max_seq_length=2048, )
| { | |
| "epoch": 0.992, | |
| "total_flos": 1.6166347506450432e+16, | |
| "train_loss": 0.15166758986250048, | |
| "train_runtime": 892.7422, | |
| "train_samples_per_second": 0.56, | |
| "train_steps_per_second": 0.069 | |
| } |