RubiNet

RubiNet is a bilingual English-Turkish conversational model release built on top of mistralai/Ministral-3-3B-Base-2512. This release is provided as a LoRA adapter and reflects the RubiNet chat tuning setup used in the local HMC-based deployment stack.

The goal of RubiNet is to provide sharper dialogue quality, stronger consistency, and better reasoning behavior than the untuned base model in local assistant usage. In the local serving stack, RubiNet can also be paired with math-oriented prompting and calculator verification for safer arithmetic handling.

Model Summary

Model name: RubiNet
Base model: mistralai/Ministral-3-3B-Base-2512
Release type: LoRA adapter
Primary languages: English, Turkish
Primary use case: text generation and chat
Inference stack: Transformers + PEFT
Tuning style: RubiNet HMC chat adaptation

Eval Results

The following benchmark scores were reported for the RubiNet setup:

Benchmark	Score
PIQA	71.55%
ARC-Easy	79.82%
GSM8K-100	24.00%

Evaluation Notes

PIQA: 1315 / 1838 correct on validation
ARC-Easy: 455 / 570 correct
GSM8K-100: 24 / 100 correct
These values come from the attached evaluation artifacts included in this repository under benchmarks/.

What This Repository Contains

This repository is intended to host the RubiNet adapter release and related reference files:

adapter_model.safetensors
adapter_config.json
tokenizer.json
tokenizer_config.json
ministral_3b_hmc_chat.py
ministral_3b_hmc_server.py
local.png
RubiNetHMC.png
benchmark result JSON files

This repository does not bundle the original base model weights. You need access to the base model mistralai/Ministral-3-3B-Base-2512 in order to load this adapter.

Loading Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "mistralai/Ministral-3-3B-Base-2512"
adapter_id = "DevHunterAI/RubiNet"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_id)

messages = [
    {"role": "user", "content": "Explain why 2+2=4 in a short way."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=128, temperature=0.0)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Chat Example

Example local RubiNet chat interface screenshot.

Architecture Overview

RubiNet HMC architecture overview used in the local serving stack.

Training / Adaptation Note

RubiNet is a fine-tuned conversational adaptation derived from mistralai/Ministral-3-3B-Base-2512. The release uses an HMC-oriented chat setup and is intended for local assistant-style interaction, bilingual usage, and reasoning-focused experimentation.

Limitations

This release is an adapter, not a full standalone base checkpoint.
Benchmark scores depend on the exact prompting and inference configuration.
Arithmetic reliability improves when RubiNet is combined with external calculator verification in the serving layer.
GSM8K performance is still limited relative to stronger specialized math-tuned models.