OpenCOReTechnologies/CORe-Predetermined-v1

<p align="center"> <img src="https://opencore.one/og-image.png" alt="CORe" width="320" /> </p>

CORe Predetermined V1

CORe Predetermined V1 is a tiny (30M-parameter) decoder-only language model from CORe Technologies, built for one job: predetermined outcomes without brittle exact-match rules.

Traditional FAQ / canned-response software matches user input against thousands of stored question strings, and breaks the moment someone types who's patricia instead of who is patricia. CORe Predetermined takes a different approach: you fine-tune it on your question/answer pairs once, and the model generalizes across phrasing, so any reasonable rewording of a covered question returns your predetermined answer.

• —Base model is already filled with a few preview Q&As (AI-fundamentals concepts) so you can test the behavior immediately, ask about them in any phrasing you like.
• —Fine-tune it on your own Q&A set to replace or extend the predetermined knowledge. A few dozen pairs is enough.
• —Runs anywhere: 120MB, CPU-friendly, no GPU required for inference.

Quick start

Note: this is a custom architecture, so trust_remote_code=True is required — without it from_pretrained will raise an error about the unknown core model type.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OpenCOReTechnologies/CORe-Predetermined-v1", trust_remote_code=True
)
model.eval()
tok = AutoTokenizer.from_pretrained("OpenCOReTechnologies/CORe-Predetermined-v1")

def ask(question, max_new_tokens=120, temperature=0.3):
    text = tok.apply_chat_template(
        [{"role": "user", "content": question}],
        add_generation_prompt=True, tokenize=False,
    )
    enc = tok(text, add_special_tokens=False, return_tensors="pt")
    out = model.generate(**enc, max_new_tokens=max_new_tokens,
                         temperature=temperature, top_k=40, do_sample=True)
    return tok.decode(out[0][enc["input_ids"].shape[1]:],
                      skip_special_tokens=True).strip()

print(ask("what's an intelligent agent?"))   # phrasing is flexible

Try the built-in preview questions

The base model ships with a small set of memorized AI-concept answers. Ask them in your own words, the point is that exact phrasing doesn't matter:

The strongly-memorized rows demonstrate the core behavior: one training example, robust retrieval across rephrasings. Fine-tuning on your own pairs moves your content into that strongly-memorized regime.

Fine-tuning your own predetermined answers

Prepare a text file of Q&A pairs in the chat format:

<|user|>
How do I reset my password?
<|assistant|>
Go to Settings → Account → Reset Password. The reset link expires in 15 minutes.
<|endoftext|>

Fine-tune with any standard causal-LM loop (the model is a plain PreTrainedModel, so Trainer, accelerate, or a hand-rolled loop all work). At 30M parameters, a full fine-tune runs on a laptop CPU in minutes to hours depending on dataset size. Low learning rates (1e-5 to 5e-5) with a few epochs over your pairs is usually enough; the model is small enough that it will memorize your set quickly while keeping phrasing robustness.

Tips:

• —20–200 pairs per topic cluster works well; you do not need thousands of exact-string variants.
• —Keep answers canonical, the model will reproduce the content of your answer even when the wording of the question changes.
• —Mix in a small amount of generic text if you want to preserve conversational fluency outside your covered topics.

Available variants

Pick the file that fits your deployment. All produce identical answers; smaller = faster CPU inference.

GGUF usage (llama.cpp, llama-cpp-python, LM Studio, Ollama, etc.):

llama-completion -m core-predetermined-v1-q4_k_m.gguf \
  -p "<|user|>\nwhat even is ai\n<|assistant|>\n" -n 120

Model details

Limitations

• —This is a 30M-parameter model. It is not a general-purpose assistant and will not compete with large models on open-ended tasks; that is not what it's for. Treat it as a flexible lookup layer over your predetermined content.
• —Outside its fine-tuned coverage it will improvise, sometimes incorrectly. For production use, gate responses on confidence or restrict usage to covered topics.
• —Training data was English-only; other languages are unsupported.

_{The architecture is registered as a first-class custom COReForCausalLM model (model_type: core) via trust_remote_code, no external framework code required beyond transformers itself.}