Qodo-Embed-1-1.5B EU AI Act Compliance Profile

Qodo-Embed-1

Qodo-Embed-1 is a state-of-the-art code embedding model designed for retrieval tasks in the software development domain. It is offered in two sizes: lite (1.5B) and medium (7B). The model is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search, retrieval-augmented generation (RAG), and contextual understanding of programming languages. This model outperforms all previous open-source models in the COIR and MTEB leaderboards, achieving best-in-class performance with a significantly smaller size compared to competing models.

Languages Supported:

• Python
• C++
• C#
• Go
• Java
• Javascript
• PHP
• Ruby
• Typescript

Model Information

• Model Size: 1.5B
• Embedding Dimension: 1536
• Max Input Tokens: 32k

Requirements

Usage

Sentence Transformers

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Qodo/Qodo-Embed-1-1.5B")
# Run inference
sentences = [
    'accumulator = sum(item.value for item in collection)',  
    'result = reduce(lambda acc, curr: acc + curr.amount, data, 0)',  
    'matrix = [[i*j for j in range(n)] for i in range(n)]'  
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1536]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Transformers

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_

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Tags

Library

Base Model