Text Generation
Safetensors
English
unsloth
job-shop-scheduling
optimization
llama
jssp
conversational
Instructions to use sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3 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 sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3 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 sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3", max_seq_length=2048, )
PFSP LLaMA 8B Fine-tuned Model
Model Description
Permutation Flow Scheduling Problem (PFSP) 최적화를 위해 파인튜닝된 LLaMA 8B 모델입니다.
Usage
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3",
max_seq_length=16384,
load_in_4bit=True,
dtype=torch.bfloat16,
)
FastLanguageModel.for_inference(model)
Model tree for sooyeon1/pfsp_neh_llama8b_default_r64_ep2_3
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct