Feature Extraction
Transformers
Safetensors
English
custom_model
multi-modal
conversational
speechllm
speech2text
custom_code
Instructions to use shangeth/SpeechLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shangeth/SpeechLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="shangeth/SpeechLLM", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shangeth/SpeechLLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Delete MyConfig.py
Browse files- MyConfig.py +0 -9
MyConfig.py
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from transformers import PretrainedConfig
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class CustomModelConfig(PretrainedConfig):
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model_type = "custom_model"
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def __init__(self, audio_enc_dim=1280, llm_dim=2048, **kwargs):
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super().__init__(**kwargs)
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self.audio_enc_dim = audio_enc_dim
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self.llm_dim = llm_dim
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