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
| { | |
| "_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5632, | |
| "max_position_embeddings": 2048, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 22, | |
| "num_key_value_heads": 4, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.38.2", | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |