DynamicMind-Mini

DynamicMind-Mini is a decoder-only model trained on FineWeb-Edu, SmolLM-Corpus and FineMath

The model has about 8.9M parameters and its based on MiniBananaMind-v4-9M architecture with a custom 8k-token byte-level BPE tokenizer with digit-aware tokenization.

Model Details

Field	Value
Parameters	8,884,992
Architecture	Custom Llama-style decoder
Layers	9
Hidden size	256
Intermediate size	768
Attention heads	8
KV heads	2
Vocabulary size	8,192
Context length	1,024
Embeddings	Tied input/output embeddings
Weight format	safetensors
Base checkpoint	MiniBananaMind-v3-9M
Continued pretraining checkpoint	Cosmopedia-v2 step 2,613

Tokenizer

DynamicMind-Mini uses the same digit-aware 8k tokenizer as MiniBananaMind-v4-9M.

Digits are kept as separate tokens so numbers do not collapse into large number tokens during tokenization.

Digit IDs:

Token	ID
1	9
2	10
3	11
4	12
5	13
6	14
7	15
8	16
9	17
0	18

Usage

This model uses custom architecture code, so load it with trust_remote_code=True.

Install dependencies:

pip install -U transformers safetensors torch

Run inference:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "DedeProGames/DynamicMind-Mini"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
).cuda().eval()

prompt = "The meaning of life is "
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids=input_ids,
        max_new_tokens=64,
        do_sample=False,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))