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🚀Только что выпущено новое семейство моделей генерации кода Salesforce (SFR-Embedding-Code), занявшее 1-е место на бенчмарке CoIR!

Модель доступна в в 2-х размерах: 2B, 400M.

Основные характеристики:
1️⃣ Модель 2B: Занимает первое место в CoIR.
2️⃣ Модель 400M: демонстрирует лучшие показатели среди моделей на 0,5B параметров.
3️⃣ Поддерживает 12 языков программирования, Python, Java, C++, JavaScript, C# и другие!

Пример Запуска:

import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

# Each query needs to be accompanied by an corresponding instruction describing the task.
query_instruction_example = "Given Code or Text, retrieval relevant content"
queries = [
"how to implement quick sort in Python?"
]

# No instruction needed for retrieval passages
passages = [
"def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)",
"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr"
]

# load model with tokenizer
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Code-2B_R', trust_remote_code=True)

# get the embeddings
max_length = 32768
query_embeddings = model.encode_queries(queries, instruction=query_instruction_example, max_length=max_length)
passage_embeddings = model.encode_corpus(passages, max_length=max_length)

# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)

scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())



Документация
Модель 400M
Модель 2B


📌Лицензирование моделей: CC-BY-NC-SA-4.0 License.

@ai_machinelearning_big_data


#CodeAI #MLResearch #SOTA #OpenScience #code #llm #ml



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🚀Только что выпущено новое семейство моделей генерации кода Salesforce (SFR-Embedding-Code), занявшее 1-е место на бенчмарке CoIR!

Модель доступна в в 2-х размерах: 2B, 400M.

Основные характеристики:
1️⃣ Модель 2B: Занимает первое место в CoIR.
2️⃣ Модель 400M: демонстрирует лучшие показатели среди моделей на 0,5B параметров.
3️⃣ Поддерживает 12 языков программирования, Python, Java, C++, JavaScript, C# и другие!

Пример Запуска:

import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

# Each query needs to be accompanied by an corresponding instruction describing the task.
query_instruction_example = "Given Code or Text, retrieval relevant content"
queries = [
"how to implement quick sort in Python?"
]

# No instruction needed for retrieval passages
passages = [
"def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)",
"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr"
]

# load model with tokenizer
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Code-2B_R', trust_remote_code=True)

# get the embeddings
max_length = 32768
query_embeddings = model.encode_queries(queries, instruction=query_instruction_example, max_length=max_length)
passage_embeddings = model.encode_corpus(passages, max_length=max_length)

# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)

scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())



Документация
Модель 400M
Модель 2B


📌Лицензирование моделей: CC-BY-NC-SA-4.0 License.

@ai_machinelearning_big_data


#CodeAI #MLResearch #SOTA #OpenScience #code #llm #ml

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