tags	license	license_name	license_link	pipeline_tag	library_name	base_model
sentence-transformers sentence-similarity feature-extraction transformers Qwen2	other	qodoai-open-rail-m	LICENSE	sentence-similarity	sentence-transformers	Alibaba-NLP/gte-Qwen2-1.5B-instruct

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

transformers>=4.39.2
flash_attn>=2.5.6

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_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


# Each query must come with a one-sentence instruction that describes the task
queries = [
      'how to handle memory efficient data streaming',
      'implement binary tree traversal'
  ]

documents = [
        """def process_in_chunks():
            buffer = deque(maxlen=1000)
            for record in source_iterator:
                buffer.append(transform(record))
                if len(buffer) >= 1000:
                    yield from buffer
                    buffer.clear()""",

        """class LazyLoader:
            def __init__(self, source):
                self.generator = iter(source)
                self._cache = []

            def next_batch(self, size=100):
                while len(self._cache) < size:
                    try:
                        self._cache.append(next(self.generator))
                    except StopIteration:
                        break
                return self._cache.pop(0) if self._cache else None""",

        """def dfs_recursive(root):
            if not root:
                return []
            stack = []
            stack.extend(dfs_recursive(root.right))
            stack.append(root.val)
            stack.extend(dfs_recursive(root.left))
            return stack"""
    ]
input_texts = queries + documents

tokenizer = AutoTokenizer.from_pretrained('Qodo/Qodo-Embed-1-1.5B', trust_remote_code=True)
model = AutoModel.from_pretrained('Qodo/Qodo-Embed-1-1.5B', trust_remote_code=True)

max_length = 8192

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

License

QodoAI-Open-RAIL-M