Instructions to use TitanML/tiny-mistral-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TitanML/tiny-mistral-embedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TitanML/tiny-mistral-embedder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TitanML/tiny-mistral-embedder") model = AutoModel.from_pretrained("TitanML/tiny-mistral-embedder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 665 Bytes
70469db | 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 | {
"_name_or_path": "openaccess-ai-collective/tiny-mistral",
"architectures": [
"MistralModel"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dropout_p": 0.1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 16,
"num_hidden_layers": 8,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.38.0",
"use_cache": true,
"vocab_size": 32000
}
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