Instructions to use baseten/RandomQwen2ForSequenceClassification-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baseten/RandomQwen2ForSequenceClassification-0.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baseten/RandomQwen2ForSequenceClassification-0.5B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("baseten/RandomQwen2ForSequenceClassification-0.5B") model = AutoModelForSequenceClassification.from_pretrained("baseten/RandomQwen2ForSequenceClassification-0.5B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct", | |
| "architectures": [ | |
| "Qwen2ForSequenceClassification" | |
| ], | |
| "id2label": { | |
| "0": "dummy_0", | |
| "1": "dummy_1" | |
| }, | |
| "label2id": { | |
| "dummy_0": 0, | |
| "dummy_1": 1 | |
| }, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151645, | |
| "hidden_act": "silu", | |
| "hidden_size": 896, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4864, | |
| "max_position_embeddings": 16384, | |
| "max_window_layers": 21, | |
| "model_type": "qwen2", | |
| "num_attention_heads": 14, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 2, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000.0, | |
| "sliding_window": null, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.1", | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 151936 | |
| } | |