---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) 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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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]
```

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### Downstream Usage (Sentence Transformers)

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<details><summary>Click to expand</summary>

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## 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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