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SmallDoge/
Doge-20M-Instruct-SFT

Name: Doge-20M-Instruct-SFT
Rating: 2.5 (26 reviews)

HuggingFaceTB/smoltalk

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Doge 20M Instruct SFT

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Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by SmallDoge community, for detailed algorithm and model architecture, paper coming soon, all training details and code are available in the small-doge repository.

Uses

python

from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M-Instruct-SFT")
model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M-Instruct-SFT", trust_remote_code=True)

generation_config = GenerationConfig(
      max_new_tokens=100, 
      use_cache=True, 
      do_sample=True, 
      temperature=0.8, 
      top_p=0.9,
      repetition_penalty=1.0
)
steamer = TextStreamer(
      tokenizer=tokenizer, 
      skip_prompt=True
)

prompt = "Hi, how are you doing today?"
conversation = [
      {"role": "user", "content": prompt}
]
inputs = tokenizer.apply_chat_template(
    conversation=conversation,
    tokenize=True,
    return_tensors="pt",
)

outputs = model.generate(
    inputs, 
    tokenizer=tokenizer,
    generation_config=generation_config, 
    streamer=steamer
)

Model Details

We build the Doge-Instruct-SFT by SFT on SmolTalk.

SFT:

Model	Training Data	Epochs	Content Length	LR	Batch Size	Precision
Doge-20M-Instruct-SFT	smoltalk	2	2048	8e-4	0.25M	bfloat16
Doge-60M-Instruct-SFT	smoltalk	2	2048	6e-4	0.25M	bfloat16
Doge-160M-Instruct-SFT	smoltalk	2	2048	4e-4	0.25M	bfloat16
Doge-320M-Instruct-SFT	smoltalk	2	2048	2e-4	0.25M	bfloat16

Procedure:

SFT:

Visualize in Weights & Biases

Environment:

Image: nvcr.io/nvidia/pytorch:24.12-py3
Hardware: 1x NVIDIA RTX 4090
Software: Transformers, TRL

Citation

bibtex

@misc{shi2024wonderfulmatrices,
      title={Wonderful Matrices: Combining for a More Efficient and Effective Foundation Model Architecture}, 
      author={Jingze Shi and Bingheng Wu},
      year={2024},
      eprint={2412.11834},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2412.11834}, 
}