Instructions to use jdchang/bt-model-lr-1e-05-step-955 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdchang/bt-model-lr-1e-05-step-955 with Transformers:
# Load model directly from transformers import AutoTokenizer, Qwen2ForQSharp tokenizer = AutoTokenizer.from_pretrained("jdchang/bt-model-lr-1e-05-step-955") model = Qwen2ForQSharp.from_pretrained("jdchang/bt-model-lr-1e-05-step-955") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "Qwen2ForQSharp" | |
| ], | |
| "attention_dropout": 0.05, | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151643, | |
| "hidden_act": "silu", | |
| "hidden_size": 1536, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 8960, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "max_position_embeddings": 131072, | |
| "max_window_layers": 21, | |
| "model_type": "qwen2", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 2, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 10000, | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.51.3", | |
| "use_cache": false, | |
| "use_mrope": false, | |
| "use_sliding_window": false, | |
| "vocab_size": 151936 | |
| } | |