Text Classification
setfit
Safetensors
sentence-transformers
qwen3
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use fefofico/nuclear_trained_f2llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use fefofico/nuclear_trained_f2llm with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("fefofico/nuclear_trained_f2llm") - sentence-transformers
How to use fefofico/nuclear_trained_f2llm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fefofico/nuclear_trained_f2llm") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "Qwen3Model" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 151643, | |
| "dtype": "float32", | |
| "eos_token_id": 151645, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 320, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "layer_types": [ | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 40960, | |
| "max_window_layers": 8, | |
| "model_type": "qwen3", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 8, | |
| "num_key_value_heads": 8, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000, | |
| "sliding_window": null, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "4.57.6", | |
| "use_cache": false, | |
| "use_sliding_window": false, | |
| "vocab_size": 151936 | |
| } | |