# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("crumb/shrink-init", trust_remote_code=True, dtype="auto")Quick Links
A randomly initialized checkpoint of a 252M custom transformer architecture with two linear transformations from the llama2-70b embeddings to 1024-dimensional space from 8192-d and then back from 1024-d to 8192-d for the llama2-70b language modelling head.
To be trained
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="crumb/shrink-init", trust_remote_code=True)