README.md

metadata

language:
  - en
  - tr
tags:
  - text-generation
  - conversational
  - english
  - turkish
  - mistral
  - peft
  - lora
  - hmc
  - reasoning
  - mathematical-reasoning
datasets:
  - piqa
  - ai2_arc
  - gsm8k
base_model:
  - mistralai/Ministral-3-3B-Base-2512
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: RubiNet
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          type: piqa
          name: PIQA
          split: validation
        metrics:
          - type: accuracy
            name: Accuracy
            value: 71.55
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          type: ai2_arc
          name: ARC-Easy
          config: ARC-Easy
          split: test
        metrics:
          - type: accuracy
            name: Accuracy
            value: 79.82
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          type: gsm8k
          name: GSM8K-100
          split: test
        metrics:
          - type: accuracy
            name: Accuracy
            value: 24

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
• 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

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

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.

Repository Notes

If you publish this repository publicly, keep the model title as RubiNet and place extra technical details such as benchmark scores, language coverage, and architecture hints in the tags and description rather than in the title.