Instructions to use SangBinCho/long_context_16k_testing_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SangBinCho/long_context_16k_testing_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/mnt/local_storage/.cache/huggingface/hub/models--meta-llama--Llama-2-13b-chat-hf/snapshots/ee41e514ee23be967cc6a698dba34c5fd08ab9b5") model = PeftModel.from_pretrained(base_model, "SangBinCho/long_context_16k_testing_2") - Notebooks
- Google Colab
- Kaggle
File size: 772 Bytes
25971d3 | 1 | {"_name_or_path": "/mnt/local_storage/.cache/huggingface/hub/models--meta-llama--Llama-2-13b-chat-hf/snapshots/ee41e514ee23be967cc6a698dba34c5fd08ab9b5", "architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 1, "eos_token_id": 2, "hidden_act": "silu", "hidden_size": 5120, "initializer_range": 0.02, "intermediate_size": 13824, "max_position_embeddings": 32768.0, "model_type": "llama", "num_attention_heads": 40, "num_hidden_layers": 40, "num_key_value_heads": 40, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 8.0, "type": "linear"}, "rope_theta": 10000.0, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.37.0.dev0", "use_cache": false, "vocab_size": 32000} |