HuggingFaceTB/everyday-conversations-llama3.1-2k
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How to use Vagabondworker/Vaga with Adapters:
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("undefined")
model.load_adapter("Vagabondworker/Vaga", set_active=True)How to use Vagabondworker/Vaga with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("XLabs-AI/flux-RealismLora,mattshumer/Reflection-Llama-3.1-70B,MikeRoz/mattshumer_Reflection-Llama-3.1-70B-8.0bpw-h8-exl2,meta-llama/Meta-Llama-3.1-8B-Instruct,Downtown-Case/meta-llama_Meta-Llama-3.1-8B-Instruct-exl2-8bpw", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Vagabondworker/Vaga")
prompt = "-"
image = pipe(prompt).images[0]pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("XLabs-AI/flux-RealismLora,mattshumer/Reflection-Llama-3.1-70B,MikeRoz/mattshumer_Reflection-Llama-3.1-70B-8.0bpw-h8-exl2,meta-llama/Meta-Llama-3.1-8B-Instruct,Downtown-Case/meta-llama_Meta-Llama-3.1-8B-Instruct-exl2-8bpw", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Vagabondworker/Vaga")
prompt = "-"
image = pipe(prompt).images[0]





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