Feature Extraction
MLX
sentence-transformers
xlm-roberta
embeddings
multilingual
quantized
int8
q8
revis
text-embeddings-inference
Instructions to use mavis-ai/Multilingual-e5-large-Q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mavis-ai/Multilingual-e5-large-Q8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Multilingual-e5-large-Q8 mavis-ai/Multilingual-e5-large-Q8
- sentence-transformers
How to use mavis-ai/Multilingual-e5-large-Q8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mavis-ai/Multilingual-e5-large-Q8") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| { | |
| "return_dict": true, | |
| "output_hidden_states": false, | |
| "output_attentions": false, | |
| "torchscript": false, | |
| "torch_dtype": "float32", | |
| "use_bfloat16": false, | |
| "tf_legacy_loss": false, | |
| "pruned_heads": {}, | |
| "tie_word_embeddings": true, | |
| "is_encoder_decoder": false, | |
| "is_decoder": false, | |
| "cross_attention_hidden_size": null, | |
| "add_cross_attention": false, | |
| "tie_encoder_decoder": false, | |
| "max_length": 20, | |
| "min_length": 0, | |
| "do_sample": false, | |
| "early_stopping": false, | |
| "num_beams": 1, | |
| "num_beam_groups": 1, | |
| "diversity_penalty": 0.0, | |
| "temperature": 1.0, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "typical_p": 1.0, | |
| "repetition_penalty": 1.0, | |
| "length_penalty": 1.0, | |
| "no_repeat_ngram_size": 0, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "bad_words_ids": null, | |
| "num_return_sequences": 1, | |
| "chunk_size_feed_forward": 0, | |
| "output_scores": false, | |
| "return_dict_in_generate": false, | |
| "forced_bos_token_id": null, | |
| "forced_eos_token_id": null, | |
| "remove_invalid_values": false, | |
| "exponential_decay_length_penalty": null, | |
| "suppress_tokens": null, | |
| "begin_suppress_tokens": null, | |
| "architectures": [ | |
| "XLMRobertaModel" | |
| ], | |
| "finetuning_task": null, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "tokenizer_class": null, | |
| "prefix": null, | |
| "bos_token_id": 0, | |
| "pad_token_id": 1, | |
| "eos_token_id": 2, | |
| "sep_token_id": null, | |
| "decoder_start_token_id": null, | |
| "task_specific_params": null, | |
| "problem_type": null, | |
| "_name_or_path": "/Users/katopz/.cache/huggingface/hub/models--intfloat--multilingual-e5-large/snapshots/9f78368af0062735ba99812349c562316e29f719", | |
| "transformers_version": "4.36.2", | |
| "model_type": "xlm-roberta", | |
| "output_past": true, | |
| "vocab_size": 250002, | |
| "hidden_size": 1024, | |
| "num_hidden_layers": 24, | |
| "num_attention_heads": 16, | |
| "hidden_act": "gelu", | |
| "intermediate_size": 4096, | |
| "hidden_dropout_prob": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "max_position_embeddings": 514, | |
| "type_vocab_size": 1, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-05, | |
| "position_embedding_type": "absolute", | |
| "use_cache": true, | |
| "classifier_dropout": null, | |
| "revis_quantization": { | |
| "format": "revis-xlm-roberta-e5-mlx-native-q8", | |
| "bits": 8, | |
| "group_size": 64, | |
| "mode": "affine", | |
| "type": "mlx-native-affine", | |
| "manifest": "quantization.json" | |
| } | |
| } | |