Feature Extraction
Transformers
Safetensors
sentence-transformers
English
Hindi
bidir_mistral
sentence-similarity
custom_code
Instructions to use Sailesh97/Hinvec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sailesh97/Hinvec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Sailesh97/Hinvec", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sailesh97/Hinvec", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Sailesh97/Hinvec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sailesh97/Hinvec", trust_remote_code=True) 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
File size: 838 Bytes
cdbec7c b058a2f cdbec7c | 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 | {
"_name_or_path": "LingoIITGN/Ganga-2-1B",
"architectures": [
"BidirectionalMistralModel"
],
"auto_map": {
"AutoConfig": "configuration_hinvec.BidirectionalMistralConfig",
"AutoModel": "modeling_hinvec.BidirectionalMistralModel"
},
"attention_dropout": 0.2,
"bos_token_id": 6,
"eos_token_id": 3,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 7168,
"max_position_embeddings": 2048,
"model_type": "bidir_mistral",
"num_attention_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"sliding_window": 1024,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.49.0",
"use_cache": true,
"vocab_size": 32768
}
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