Instructions to use rendchevi/text-to-code-v0.1-lora 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-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rendchevi/text-to-code-v0.1-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 200, checkpoint
Browse files
last-checkpoint/adapter/adapter_model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/speaker_projector.pt
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last-checkpoint/trainer_state.json
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 115.95,
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"eval_steps_per_second": 14.517,
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"step": 100
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}
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"logging_steps": 100,
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"global_step": 200,
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"eval_samples_per_second": 115.95,
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{
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"grad_norm": 0.14562372863292694,
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