Completetinymodelraven Top Now

Unlike standard decoder-only models, the Raven architecture utilizes a Recursive Attention with Variable Extraction Nodes (RAVEN). This allows the model to maintain a longer effective context window (up to 8k tokens) without the quadratic blowup of standard attention. The "Top" variant trims the top 2 layers during inference, reducing latency by 30%.

inputs = tokenizer("Explain quantum computing in one sentence:", return_tensors="pt").to("cuda") completetinymodelraven top

outputs = model.generate( **inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95, temperature=0.7 ) "In twilight's hush, where shadows play Amidst the

print(tokenizer.decode(outputs[0], skip_special_tokens=True)) "In twilight's hush

"In twilight's hush, where shadows play
Amidst the whispers of a dying day
The raven's call, a mystic's sigh
Echoes through, a lonely sky

With eyes like jewels, dark and bright
It watches worlds, in endless night
A symbol of mystery, a bird of might
The raven's wisdom, a guiding light

In completion of the cycle, it stands
A sentinel of mystic lands
A completion model, of secrets untold
The raven's wisdom, forever to hold."