DynamicMind-Mini-Instruct

DynamicMind-Mini-Instruct is the instruction-tuned version of DynamicMind-Mini. It was fully fine-tuned on 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

Usage

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

pip install -U transformers safetensors torch

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.