Instructions to use kevin009/llama322 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use kevin009/llama322 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 kevin009/llama322 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 kevin009/llama322 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kevin009/llama322 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="kevin009/llama322", max_seq_length=2048, )
kevin009/llama322
Model Description
This is a LoRA-tuned version of kevin009/llama322 using KTO (Kahneman-Tversky Optimization).
Training Parameters
- Learning Rate: 5e-06
- Batch Size: 1
- Training Steps: 2043
- LoRA Rank: 16
- Training Date: 2024-12-29
Usage
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("kevin009/llama322", token="YOUR_TOKEN")
tokenizer = AutoTokenizer.from_pretrained("kevin009/llama322")
Inference Providers NEW
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