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)# pip install -U transformers accelerate # 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
Download config.py from skit-ai/speechllm-2B: direct link, hf CLI and curl.
- Browser
- Download file 526 Bytes
-
https://huggingface.co/skit-ai/speechllm-2B/resolve/main/config.py
- Command line
-
hf download hf://skit-ai/speechllm-2B/config.py
-
curl -L -o config.py https://huggingface.co/skit-ai/speechllm-2B/resolve/main/config.py
526 Bytes
| from transformers import PretrainedConfig | |
| class SpeechLLMModelConfig(PretrainedConfig): | |
| model_type = "custom_model" | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| self.audio_enc_dim = 1280 | |
| self.llm_dim = 2048 | |
| self.audio_processor_name = "facebook/hubert-large-ls960-ft" | |
| self.audio_encoder_name = 'facebook/hubert-xlarge-ll60k' | |
| self.llm_model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" | |
| self.llm_model_checkpoint = "hf_repo/llm_model_checkpoint" | |