Instructions to use Mutonix/Vriptor-STLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mutonix/Vriptor-STLLM with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Mutonix/Vriptor-STLLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/mnt/nlp-ali/usr/yangdongjie/video/ST-LLM/stllm/output/vript_bsz128_lr2e5_zero2_offload_freeze_vit_qformer_v2_no_mvm/", | |
| "architectures": [ | |
| "STLLMForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "bos_token_id": 0, | |
| "eos_token_id": 1, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 8192, | |
| "max_sequence_length": 8192, | |
| "model_type": "st_llm_hf", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": -1, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": { | |
| "factor": 4.0, | |
| "type": "dynamic" | |
| }, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.32.0", | |
| "use_cache": true, | |
| "vocab_size": 32001 | |
| } | |