Model Card for Toffee 3b β

We've finally completed Toffee β, a series of language models trained to act as helpful assistants. Toffee 3B β is the first model in the series, a refined version of mistralai/Ministral-3-3B-Base-2512, which was post-trained on high-quality instructional datasets.

In fact, we were trying to achieve the title of "The Gold Standard of Small Chat-models" and you know, we succeeded, the model scored excellent results on benchmarks for its number of parameters.

Model description

• Model: maxzt/toffee-3b-beta
• Language(s) (NLP): Primarily English
• License: MIT
• Finetuned from model: mistralai/Ministral-3-3B-Base-2512

📊 MMLU & HellaSwag benchmarks results

Toffee-3B-β: MMLU 63.10%, HellaSwag 72.04%, BoolQ 84.98%, Winogrande 68.11%.

Results for the other models are taken from published model cards / benchmark reports. Evaluation settings may differ between models (shots, prompt format, evaluator version), so the table is an approximate comparison rather than a strictly controlled leaderboard.

Technical Specifications

Training Details

Usage

import torch

from transformers import (
    Mistral3ForConditionalGeneration,
    MistralCommonBackend,
)

model_id = "maxzt/toffee-3b-beta"

tokenizer = MistralCommonBackend.from_pretrained(
    model_id,
)

model = Mistral3ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

user_message = "Explain why the sky is blue."

prompt = f"[INST] {user_message} [/INST] "

inputs = tokenizer.encode(
    prompt,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

response = tokenizer.decode(
    outputs[0][inputs.shape[-1]:]
)

print(response.strip())

Training Data

Toffee-3B-β was trained on a curated mixture of instruction-following, mathematical reasoning, and programming data.

The datasets were converted into a unified prompt/completion format, shuffled with a fixed seed (42), and split into 98% training / 2% evaluation data.

Examples longer than 8,192 tokens were removed. During SFT, loss was applied only to assistant completion tokens.

Citation

@misc{toffee3bbeta,
  title={Toffee 3B β},
  author={Max ZT},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/maxzt/toffee-3b-beta}
}