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
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Try the model in [Google Colab Notebook](https://colab.research.google.com/drive/1uqhRl36LJKA4IxnrhplLMv0wQ_f3OuBM?usp=sharing).
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## Model Details
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Try the model in [Google Colab Notebook](https://colab.research.google.com/drive/1uqhRl36LJKA4IxnrhplLMv0wQ_f3OuBM?usp=sharing). Also, check out our [blog](https://tech.skit.ai/speech-conversational-llms/) on SpeechLLM for end-to-end conversational agents(User Speech -> Response).
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## Model Details
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