Instructions to use SmallDoge/Doge-60M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmallDoge/Doge-60M-Instruct with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="SmallDoge/Doge-60M-Instruct", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-60M-Instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-60M-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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| Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
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| [Doge-20M-Instruct-SFT](https://huggingface.co/JingzeShi/Doge-20M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 8e-4 | 0.25M | bfloat16 |
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| [Doge-60M-Instruct-SFT](https://huggingface.co/JingzeShi/Doge-60M-Instruct) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 6e-4 | 0.25M | bfloat16 |
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| [Doge-20M-Instruct-SFT](https://huggingface.co/JingzeShi/Doge-20M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 8e-4 | 0.25M | bfloat16 |
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| [Doge-60M-Instruct-SFT](https://huggingface.co/JingzeShi/Doge-60M-Instruct-SFT) | [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | 2 | 2048 | 6e-4 | 0.25M | bfloat16 |
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**DPO**:
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| Model | Training Data | Epochs | Content Length | LR | Batch Size | Precision |
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