Sentence Similarity
sentence-transformers
Safetensors
mistralbidirectional
swe-bench
code-similarity
code-retrieval
code-search
code-explanation
custom_code
Instructions to use nvidia/NV-EmbedCode-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/NV-EmbedCode-7b-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/NV-EmbedCode-7b-v1", 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: 817 Bytes
4b8390f | 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 | {
"_name_or_path": "nvidia/NV-EmbedCode-7b-v1",
"architectures": [
"MistralBiDirectionalModel"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "bidirectional_models.MistralBiDirectionalConfig",
"AutoModel": "bidirectional_models.MistralBiDirectionalModel"
},
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistralbidirectional",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.37.2",
"use_cache": true,
"vocab_size": 32000
}
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