Text Generation
Safetensors
English
parallel-machine-shop-scheduling
optimization
llama
unsloth
jssp
conversational
Instructions to use satgothyun/pmsp_llama8b_lora_10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use satgothyun/pmsp_llama8b_lora_10000 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 satgothyun/pmsp_llama8b_lora_10000 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 satgothyun/pmsp_llama8b_lora_10000 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for satgothyun/pmsp_llama8b_lora_10000 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="satgothyun/pmsp_llama8b_lora_10000", max_seq_length=2048, )
PMSP LLaMA 8B Fine-tuned Model
Model Description
PMSP ์ต์ ํ๋ฅผ ์ํด ํ์ธํ๋๋ LLaMA 8B ๋ชจ๋ธ์ ๋๋ค.
Training Details
- Base Model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
- LoRA Rank: 64
- Epochs: 4
- Max Sequence Length: 8,192
- Dataset: ACCORD
Usage
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="satgothyun/pmsp_llama8b_lora_10000",
max_seq_length=8192,
load_in_4bit=True,
dtype=torch.bfloat16,
)
FastLanguageModel.for_inference(model)
Model tree for satgothyun/pmsp_llama8b_lora_10000
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct