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
File size: 557 Bytes
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"architectures": [
"SpeakerConditionedCausalLM"
],
"base_model_name_or_path": "neuphonic/neutts-nano",
"bos_token_id": 128000,
"dtype": "float32",
"eos_token_id": 128261,
"freeze_base_model": true,
"hidden_size": 576,
"model_type": "speaker_conditioned_wrapper",
"pad_token_id": 128001,
"speaker_dropout": 0.0,
"speaker_embedding_dim": 256,
"speaker_hidden_dim": null,
"speaker_token": "<|SPEAKER_TOKEN_POS|>",
"speaker_token_id": 194246,
"transformers_version": "5.6.2",
"use_cache": false,
"vocab_size": 194256
}
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