Text Classification
Transformers
Safetensors
German
llama
feature-extraction
reranker
cross-encoder
german
retrieval
rag
on-prem
text-embeddings-inference
Instructions to use keyvan-ai/Mankei-326M-Reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keyvan-ai/Mankei-326M-Reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keyvan-ai/Mankei-326M-Reranker")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("keyvan-ai/Mankei-326M-Reranker") model = AutoModel.from_pretrained("keyvan-ai/Mankei-326M-Reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 717 Bytes
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"architectures": [
"LlamaModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 0,
"dtype": "bfloat16",
"eos_token_id": 1,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 960,
"initializer_range": 0.02,
"intermediate_size": 2560,
"max_position_embeddings": 2048,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 15,
"num_hidden_layers": 30,
"num_key_value_heads": 5,
"pad_token_id": 1,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 100000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers_version": "5.15.0",
"use_cache": false,
"vocab_size": 32768
}
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