Feature Extraction
Transformers
Safetensors
English
custom_model
multi-modal
speech-language
custom_code
Eval Results (legacy)
Instructions to use skit-ai/speechllm-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skit-ai/speechllm-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skit-ai/speechllm-2B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("skit-ai/speechllm-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d5ffe0412317f76eb28fd65022f72daa21c5c7a96c96b536cc0347a78c33d4e
- Size of remote file:
- 4.4 GB
- SHA256:
- 0cc5499430aafb7b13c0203e34ff4cfea26def3c7f233c9ee45f24417110b6e0
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