OASIS-code-1.3B

307
13
1.3B
llama
by
Kwaipilot
Embedding Model
OTHER
1.3B params
New
307 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
3GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Code Examples

Usagebash
pip install -U torch
pip install -U transformers
Usagebash
pip install -U torch
pip install -U transformers
tensor([0.6495, 0.8036])bash
pip install -U sentence-transformers
tensor([0.6495, 0.8036])bash
pip install -U sentence-transformers
Sentence Transformerspython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Kwaipilot/OASIS-code-1.3B")#, model_kwargs={"torch_dtype": torch.bfloat16})

query = "How to do quicksort in python?"

code1 = """def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        swapped = False
        for j in range(1, n - i):
            if arr[j - 1] > arr[j]:
                arr[j - 1], arr[j] = arr[j], arr[j - 1]
                swapped = True
        if not swapped:
            break
    return arr"""

code2 = """def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    else:
        pivot = arr[0]
        less = [x for x in arr[1:] if x <= pivot]
        greater = [x for x in arr[1:] if x > pivot]
        return quick_sort(less) + [pivot] + quick_sort(greater)"""

# Run inference
query_embedding = model.encode([query], prompt_name="query")
code_embeddings = model.encode([code1, code2])

print(code_embeddings.shape)
# (2, 2048)

# Get the similarity scores for the embeddings
print(model.similarity(query_embedding[0], code_embeddings[0]))
print(model.similarity(query_embedding[0], code_embeddings[1]))
# tensor([[0.6495]])
# tensor([[0.8036]])
Sentence Transformerspython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Kwaipilot/OASIS-code-1.3B")#, model_kwargs={"torch_dtype": torch.bfloat16})

query = "How to do quicksort in python?"

code1 = """def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        swapped = False
        for j in range(1, n - i):
            if arr[j - 1] > arr[j]:
                arr[j - 1], arr[j] = arr[j], arr[j - 1]
                swapped = True
        if not swapped:
            break
    return arr"""

code2 = """def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    else:
        pivot = arr[0]
        less = [x for x in arr[1:] if x <= pivot]
        greater = [x for x in arr[1:] if x > pivot]
        return quick_sort(less) + [pivot] + quick_sort(greater)"""

# Run inference
query_embedding = model.encode([query], prompt_name="query")
code_embeddings = model.encode([code1, code2])

print(code_embeddings.shape)
# (2, 2048)

# Get the similarity scores for the embeddings
print(model.similarity(query_embedding[0], code_embeddings[0]))
print(model.similarity(query_embedding[0], code_embeddings[1]))
# tensor([[0.6495]])
# tensor([[0.8036]])

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