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 🤗 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

velvetScar
/

llm2vec-llama-3.1-8B