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
File size: 711 Bytes
640fb4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"base_model_name_or_path": "neuphonic/neutts-nano",
"bos_token_id": 128000,
"eos_token_id": 128261,
"freeze_base_model": true,
"hidden_size": 576,
"inference_mode": false,
"lora_alpha": 32,
"lora_bias": "none",
"lora_dropout": 0.05,
"lora_r": 16,
"lora_target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj"
],
"lora_task_type": "CAUSAL_LM",
"model_type": "speaker_conditioned_lora_wrapper",
"pad_token_id": 128001,
"speaker_dropout": 0.25,
"speaker_embedding_dim": 256,
"speaker_hidden_dim": 512,
"speaker_token": "<|SPEAKER_TOKEN_POS|>",
"speaker_token_id": 194246,
"transformers_version": "5.6.2",
"use_cache": false,
"vocab_size": 194256
}
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