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language:
- en
- tr
tags:
- text-generation
- conversational
- english
- turkish
- mistral
- peft
- lora
- hmc
- reasoning
- mathematical-reasoning
datasets:
- HuggingFaceH4/ultrachat_200k
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`
- `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. |