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
Training in progress, step 500
Browse files- config.json +17 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
config.json
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{
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"architectures": [
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"SpeakerConditionedCausalLM"
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],
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"base_model_name_or_path": "neuphonic/neutts-nano",
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"dtype": "float32",
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"freeze_base_model": true,
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"hidden_size": null,
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"injection_strategy": "replace_special_token",
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"model_type": "speaker_conditioned_causal_lm",
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"projector_dropout": 0.25,
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"projector_hidden_dim": 512,
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"speaker_embedding_dim": 256,
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"speaker_token_id": 194246,
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"transformers_version": "5.6.2",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:54a30c43e6d775c34e6621ff8caca84543f52f32df2e13b31f74f113453d149a
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size 916532448
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:842f6b1bf998f6d805426595920287dcfa3ec221fc4ff15910b9b128ee895468
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size 5265
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