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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class SpeechLLMModelConfig(PretrainedConfig): | |
| model_type = "custom_model" | |
| def __init__(self, audio_enc_dim=1280, llm_dim=2048, **kwargs): | |
| super().__init__(**kwargs) | |
| self.audio_enc_dim = audio_enc_dim | |
| self.llm_dim = llm_dim | |