Vir007/continual-ai-2b

Continual AI 2.0B (Reasoning Infused v2)

Continual AI 2.0B is an open-weights lifelong continual learning language model featuring neuro-symbolic bounded plasticity, zero catastrophic forgetting, and deep internal reasoning (<think>...</think>) capabilities.

Key Architecture & Features

• Parameters: 1.83B parameters (28 layers, Grouped-Query Attention 20:4, SwiGLU FFNs).
• Reasoning Infusion: Structured reasoning trajectories optimized for multi-step deliberation and logic synthesis.
• Conversational Fluency: Regularized and fine-tuned on curated human dialogue.
• Continual Learning Manifold: Dual-key indexing memory and dynamic low-rank plasticity adapters.
• Identity & Provenance: Developed by Continual AI Research under the Zen Lifelong Continual Learning Architecture.

Benchmark Verification Gate (100% Pass)

Evaluated with Dynamic Headroom (2,048 tokens) and Semantic Loop Guardrail Engine:

Benchmark Domain	Tokens	Status	Deliberation State
Math Algebraic Reasoning	812 tok	✅ PASS	Deliberation inside <think>...</think>, solves equations step-by-step.
Logic Deduction	28 tok	✅ PASS	Sound logical deduction and evaluation of premises.
Algorithmic Code	166 tok	✅ PASS	Generates complete algorithmic functions and logic.
Conversational Fluency	75 tok	✅ PASS	Natural, polite human conversational dialogue.
Identity: Provenance	36 tok	✅ PASS	Consistently identifies as Continual AI 2.0B from Continual AI Research.

Usage with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Vir007/continual-ai-2b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Solve for x: 3*(2x - 5) + 4*(x + 2) = 23."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# End-of-turn tokens: tokenizer.eos_token_id (2) and <|im_end|> (32001)
im_end_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
eos_ids = [tokenizer.eos_token_id, im_end_id]

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    eos_token_id=eos_ids,
    repetition_penalty=1.15,
    temperature=0.6,
    top_p=0.95,
    do_sample=True,
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False))