Instructions to use bkhmsi/micro-smollm2-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bkhmsi/micro-smollm2-360m with Transformers:
# Load model directly from transformers import AutoTokenizer, MiCRoLlama tokenizer = AutoTokenizer.from_pretrained("bkhmsi/micro-smollm2-360m") model = MiCRoLlama.from_pretrained("bkhmsi/micro-smollm2-360m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "ablate": [ | |
| "none" | |
| ], | |
| "architectures": [ | |
| "MiCRoLlama" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "backbone_num_layers": 32, | |
| "bos_token_id": 0, | |
| "config_path": "../mixture-of-reasoners/configs/config_micro_smollm2_360m.yml", | |
| "eos_token_id": 0, | |
| "gradient_checkpointing": false, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 960, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2560, | |
| "is_llama_config": true, | |
| "jitter_noise": 0, | |
| "loss_method": "all", | |
| "loss_type": "ForCausalLMLoss", | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 15, | |
| "num_experts": 4, | |
| "num_experts_per_tok": 1, | |
| "num_hidden_layers": 128, | |
| "num_key_value_heads": 5, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_interleaved": false, | |
| "rope_scaling": null, | |
| "rope_theta": 100000, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.53.2", | |
| "use_bfloat16": true, | |
| "use_cache": true, | |
| "use_router": true, | |
| "vocab_size": 49152 | |
| } | |