Raptor

Raptor is a 1.027B-parameter decoder-only causal language model with approximately 404M active parameters per token. It uses multi-head latent attention and six SwiGLU experts per layer with top-2 routing.

This revision contains the English instruction-tuned checkpoint. It was initialized from the Raptor step-35,000 pretrained checkpoint and supervised fine-tuned for one epoch on a curated SmolTalk mixture. The retained checkpoint is SFT step 7,500, selected by validation loss.

Architecture

• 18 layers
• hidden size 1,024
• latent attention dimension 256
• 16 attention heads
• six experts per layer, top-2 routing
• expert hidden size 2,816
• context length 2,048
• 35,000-token SentencePiece vocabulary
• 1.027B total parameters, about 404M active per token

Fine-tuning

• Base checkpoint: pretraining step 35,000
• Training examples: 511,721
• Validation examples: 2,000
• SFT epochs: 1
• Best checkpoint: step 7,500
• Best validation loss: 1.0597
• Best validation perplexity: 2.8855
• Training format: assistant-only loss over ### User: and ### Assistant: conversations

Usage

The architecture and tokenizer use custom code, so loading requires trust_remote_code=True.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Voyager466920/Raptor"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "What is the capital of France?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)
output = model.generate(
    inputs,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)
print(tokenizer.decode(output[0, inputs.shape[1]:], skip_special_tokens=True))

Limitations

• This is an experimental 1B-scale model and may fail simple reasoning or arithmetic tasks.
• Multi-turn memory and role consistency are unreliable.
• Responses may become verbose, repetitive, inaccurate, biased, or unsafe.
• The model is English-focused. The tokenizer has poor Korean coverage and maps many Korean words to the unknown token.
• The architecture currently recomputes the full prefix during generation and does not implement a KV cache.

License

No model license has been selected yet. Public availability does not grant additional usage rights beyond applicable law.