Instructions to use nytopop/1b_or_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nytopop/1b_or_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="nytopop/1b_or_base", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nytopop/1b_or_base") model = AutoModelForCausalLM.from_pretrained("nytopop/1b_or_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +1 -0
- config.json +1 -1
README.md
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# noteworthy changes
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- tokenizer omits `<|audio|>` to prevent finetunes resizing the embeddings unneccessarily
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- `generation_config.json` uses the correct EOS to end generation on end of audio + has a reasonable default temperature
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- `chat_template.jinja` maps user messages to transcripts and assistant messages to audio for simple conversational context management
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# noteworthy changes
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- tokenizer omits `<|audio|>` to prevent finetunes resizing the embeddings unneccessarily
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- `config.json` uses the correct EOS to end generation on end of audio
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- `generation_config.json` uses the correct EOS to end generation on end of audio + has a reasonable default temperature
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- `chat_template.jinja` maps user messages to transcripts and assistant messages to audio for simple conversational context management
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config.json
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id":
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128258,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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