Instructions to use Purusharth/gorilla-espro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Purusharth/gorilla-espro with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gorilla-llm/gorilla-openfunctions-v2") model = PeftModel.from_pretrained(base_model, "Purusharth/gorilla-espro") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "Purusharth/gorilla-espro", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "bos_token_id": 100000, | |
| "eos_token_id": 100015, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 4096, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 30, | |
| "num_key_value_heads": 32, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.34.1", | |
| "use_cache": false, | |
| "vocab_size": 102400 | |
| } |