Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
splade
sparse-encoder
code
custom_code
text-embeddings-inference
Instructions to use naver/splade-code-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-code-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("naver/splade-code-8B", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use naver/splade-code-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver/splade-code-8B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("naver/splade-code-8B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("naver/splade-code-8B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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license: cc-by-nc-sa-4.0
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---
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```python
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from transformers import AutoModelForCausalLM, AutoModel
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import os
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import torch
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splade = AutoModelForCausalLM.from_pretrained("naver/splade-code-8B", token=token, trust_remote_code=True)
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device = (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu"))
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splade.to(device)
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splade.eval()
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license: cc-by-nc-sa-4.0
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---
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SPLADE-Code-8B is a sparse retrieval model designed for code retrieval tasks.
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```python
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from transformers import AutoModelForCausalLM, AutoModel
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import os
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import torch
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splade = AutoModelForCausalLM.from_pretrained("naver/splade-code-8B", trust_remote_code=True)
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device = (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu"))
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splade.to(device)
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splade.eval()
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