A
reasoning-augmented tokenizer
built upon
SmolLM2-360M-Instruct
,
extended with explicit reasoning tokens to support
multi-stage chain-of-thought reasoning
and
structured context injection
.
This tokenizer powers the
Aries family
of reasoning-centric language models.
🔤 Special Reasoning Tokens
Token
ID
Purpose
<think>
49152
Begin inner reasoning sequence / chain of thought
<context>
49153
Inject retrieved or contextual knowledge
<answer>
49154
Mark transition to user-facing response
<end>
49155
Explicit reasoning termination signal
These tokens enable separation between latent reasoning, contextual grounding, and explicit answers — a key design for multi-path autoregressive reasoning.
from transformers import AutoTokenizer
# Load Aries Reasoning Tokenizer
tokenizer = AutoTokenizer.from_pretrained("ziadrone/aries-reasoning-tokenizer")
text = "<think> why does the moon follow us at night? <answer> because of relative motion."
ids = tokenizer.encode(text)
print("Token IDs:", ids)
print("Decoded:", tokenizer.decode(ids))
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