Instructions to use leafyseay/LaME-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leafyseay/LaME-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leafyseay/LaME-2B")# Load model directly from transformers import LaMEMultimodal model = LaMEMultimodal.from_pretrained("leafyseay/LaME-2B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "LaMEMultimodal" | |
| ], | |
| "ctl_loss_weight": 1.0, | |
| "decode_loss_weight": 1.0, | |
| "decode_target": "answer", | |
| "decoder_gradient_checkpointing": false, | |
| "decoder_max_think_tokens": 1024, | |
| "decoder_model_path": "Qwen/Qwen3-0.6B", | |
| "decoder_projector_hidden_dim": 2048, | |
| "decoder_projector_num_layers": 1, | |
| "diversity_loss_weight": 0.1, | |
| "dtype": "bfloat16", | |
| "embedding_token_id": 151665, | |
| "enable_decode": true, | |
| "freeze_backbone": false, | |
| "freeze_decoder": false, | |
| "lora_decoder_enable": false, | |
| "model_type": "openlame", | |
| "normalize": true, | |
| "num_new_special_tokens": 9, | |
| "num_reason_tokens": 8, | |
| "pooling_strategy": "special_token", | |
| "projection_activation": "gelu", | |
| "projection_dim": 3584, | |
| "projection_dropout": 0.0, | |
| "projection_hidden_dim": null, | |
| "projection_num_layers": 1, | |
| "reason_ctl_loss_weight": 1.0, | |
| "reasoning_embedding_type": "mean_pooling", | |
| "split_latent": true, | |
| "training_stage": 2, | |
| "transformers_version": "4.57.0", | |
| "trust_remote_code": true, | |
| "use_flash_attn": true, | |
| "use_projection": false, | |
| "vlm_name": "Qwen/Qwen2-VL-2B-Instruct", | |
| "vlm_torch_dtype": "bfloat16", | |
| "vocab_size": 151666 | |
| } | |