Instructions to use openbmb/MiniCPM-Reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openbmb/MiniCPM-Reranker", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("openbmb/MiniCPM-Reranker", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,015 Bytes
7243e76 1023e8b 7243e76 11cfdf9 7243e76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"_name_or_path": "openbmb/MiniCPM-Reranker",
"architectures": [
"MiniCPMForSequenceClassification"
],
"auto_map": {
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
"AutoModel": "modeling_minicpm.MiniCPMModel",
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
},
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 2304,
"initializer_range": 0.1,
"intermediate_size": 5760,
"is_causal": false,
"max_position_embeddings": 2048,
"num_attention_heads": 36,
"num_hidden_layers": 40,
"num_key_value_heads": 36,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"torch_dtype": "bfloat16",
"transformers_version": "4.36.0",
"use_cache": true,
"vocab_size": 122753,
"scale_emb": 12,
"dim_model_base": 256,
"scale_depth": 1.4,
"num_labels": 1
} |