SentenceTransformer
This is a sentence-transformers model trained. It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Maximum Sequence Length: None tokens
- Output Dimensionality: 4096 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
LLM2VecSentenceTransformer(
(0): LLM2VecWrapper(
(llm2vec_model): LLM2Vec(
(model): LlamaBiModel(
(embed_tokens): Embedding(128256, 4096)
(layers): ModuleList(
(0-31): 32 x ModifiedLlamaDecoderLayer(
(self_attn): ModifiedLlamaSdpaAttention(
(q_proj): Linear8bitLt(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear8bitLt(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear8bitLt(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear8bitLt(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear8bitLt(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear8bitLt(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear8bitLt(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLU()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
(rotary_emb): LlamaRotaryEmbedding()
)
)
)
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 馃 Hub
model = SentenceTransformer("velvetScar/llm2vec-llama-3.1-8B")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.1
- Transformers: 4.43.1
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1
Citation
BibTeX
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