README.md

---
license: apache-2.0
datasets:
- HuggingFaceTB/smol-smoltalk
base_model:
- DedeProGames/DynamicMind-Mini
---

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

# DynamicMind-Mini-Instruct
DynamicMind-Mini-Instruct is the instruction-tuned version of [DynamicMind-Mini](https://huggingface.co/DedeProGames/DynamicMind-Mini). It was fully fine-tuned on [HuggingFaceTB/smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) with loss applied only to assistant tokens and the assistant-ending EOS token.

The model has about 8.9M, a **1,024-token context window**, and a custom **8,192-token digit-aware byte-level BPE tokenizer**. It supports system prompts, multi-turn conversations, and KV-cached generation.

## 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 |

## Benchmarks

![elo_curve_plot](https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/Js7kSqswD3w8fZA5D5Pg1.png)

## Usage

This model uses custom architecture code and must be loaded with `trust_remote_code=True`.

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

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DedeProGames/DynamicMind-Mini-Instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" and torch.cuda.is_bf16_supported() else torch.float32

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=dtype,
).to(device).eval()

messages = [
    {"role": "system", "content": "You are a concise and helpful assistant."},
    {"role": "user", "content": "Hello!"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)
inputs = {name: tensor.to(device) for name, tensor in inputs.items()}

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=192,
        do_sample=False,
        repetition_penalty=1.1,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
        use_cache=True,
    )

new_tokens = output[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```

For multi-turn chat, append the generated assistant response and the next user message to `messages`, then render the chat template again.