Instructions to use Dumele/viv-updated2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dumele/viv-updated2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ") model = PeftModel.from_pretrained(base_model, "Dumele/viv-updated2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "MistralForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 50256, | |
| "eos_token_id": 50256, | |
| "gradient_checkpointing": true, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 16384, | |
| "layer_norm_epsilon": 1e-05, | |
| "max_position_embeddings": 1024, | |
| "model_type": "mistral", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 50256, | |
| "pretraining_tp": 1, | |
| "quantization_config": { | |
| "bits": 4, | |
| "damp_percent": 0.1, | |
| "desc_act": true, | |
| "group_size": 128, | |
| "model_file_base_name": "model", | |
| "model_name_or_path": null, | |
| "quant_method": "gptq", | |
| "sym": true, | |
| "true_sequential": true | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_theta": 10000.0, | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.42.0.dev0", | |
| "use_cache": false, | |
| "vocab_size": 32000 | |
| } | |