Instructions to use TRI-ML/DCLM-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TRI-ML/DCLM-1B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TRI-ML/DCLM-1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Achal Dave commited on
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README.md
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@@ -16,7 +16,7 @@ DCLM-1B is a 1.4 billion parameter language model trained on the DCLM-Baseline d
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Here are the evaluation results for DCLM-1B on various tasks (using [llm-foundry](https://github.com/mosaicml/llm-foundry) eval suite), compared to recently released small models on key benchmarks.
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As described in the paper, Core accuracy is the average of centered accuracy on
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22 tasks (including HellaSwag and ARC-E), Extended is centered accuracy averaged
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over 53 tasks.
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| Model | Params | Tokens | Open dataset? | Core | MMLU | Extended |
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Here are the evaluation results for DCLM-1B on various tasks (using [llm-foundry](https://github.com/mosaicml/llm-foundry) eval suite), compared to recently released small models on key benchmarks.
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| 17 |
As described in the paper, Core accuracy is the average of centered accuracy on
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| 18 |
22 tasks (including HellaSwag and ARC-E), Extended is centered accuracy averaged
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over 53 tasks.
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| Model | Params | Tokens | Open dataset? | Core | MMLU | Extended |
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