Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
code
modernbert
feature-extraction
code-search
code-retrieval
bug-localization
Eval Results (legacy)
text-embeddings-inference
Instructions to use vedevpatel/CodeIntel-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vedevpatel/CodeIntel-150M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vedevpatel/CodeIntel-150M") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Add Codex MCP setup
Browse files
README.md
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claude mcp add codeintel -- uvx --from "codeintel[mcp] @ git+https://github.com/vedevpatel/codeintel-150m" codeintel mcp .
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```
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## Training
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claude mcp add codeintel -- uvx --from "codeintel[mcp] @ git+https://github.com/vedevpatel/codeintel-150m" codeintel mcp .
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```
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```toml
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# Codex: ~/.codex/config.toml
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[mcp_servers.codeintel]
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command = "uvx"
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args = ["--from", "codeintel[mcp] @ git+https://github.com/vedevpatel/codeintel-150m", "codeintel", "mcp", "."]
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```
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Setup for Cursor is in the [GitHub README](https://github.com/vedevpatel/codeintel-150m#use-with-coding-agents-mcp).
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## Training
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