llm2vec-llama-3.1-8B

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

<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: None tokens
• Output Dimensionality: 4096 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

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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