Instructions to use rendchevi/text-to-code-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rendchevi/text-to-code-v0.1 with Transformers:
# Load model directly from transformers import SpeakerConditionedCausalLM model = SpeakerConditionedCausalLM.from_pretrained("rendchevi/text-to-code-v0.1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +0 -7
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README.md
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# text-to-code-v0.1
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 7.0509
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- eval_runtime: 22.5079
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- eval_samples_per_second: 111.072
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- eval_steps_per_second: 13.906
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- epoch: 0.0256
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- step: 10
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## Model description
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# text-to-code-v0.1
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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## Model description
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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