---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
- HuggingFaceTB/finemath
- HuggingFaceTB/smollm-corpus
---

![Banner](https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/0QdBKT5iKdrEy3iwLosAH.png)

# DynamicMind-Mini

DynamicMind-Mini is a decoder-only model trained on [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), [SmolLM-Corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath)

The model has about 8.9M parameters and its based on [MiniBananaMind-v4-9M](https://huggingface.co/BananaMind/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](https://huggingface.co/BananaMind/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:

```bash
pip install -U transformers safetensors torch
```

Run inference:

```python
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))
```