Feature Extraction
sentence-transformers
ONNX
Safetensors
GGUF
English
code
roberta
python
javascript
go
java
ruby
php
code-search
code-embeddings
multilingual
matryoshka
unixcoder
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use divakantpandey/code-embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use divakantpandey/code-embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("divakantpandey/code-embed-v2") 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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use divakantpandey/code-embed-v2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf divakantpandey/code-embed-v2:Q8_0 --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf divakantpandey/code-embed-v2:Q8_0 --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf divakantpandey/code-embed-v2:Q8_0 --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use divakantpandey/code-embed-v2 with Ollama:
ollama run hf.co/divakantpandey/code-embed-v2:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use divakantpandey/code-embed-v2 with Docker Model Runner:
docker model run hf.co/divakantpandey/code-embed-v2:Q8_0
- Lemonade
How to use divakantpandey/code-embed-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull divakantpandey/code-embed-v2:Q8_0
Run and chat with the model
lemonade run user.code-embed-v2-Q8_0
List all available models
lemonade list
- Atomic Chat
docs: sort model-index and scorecard with lower ranks first (Rank #3 on CoREB, #8 on CoIR, #9 on MTEB Code)
Browse files
README.md
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model-index:
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- name: code-embed-v2
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results:
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- task:
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type: retrieval
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name: MTEB(Code, v1)
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dataset:
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name: MTEB(Code, v1) Macro-Average
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type: mteb/code
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metrics:
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- type: ndcg_at_10
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value: 0.5475
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name: NDCG@10 (meanTask)
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- task:
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type: retrieval
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name: CoIR
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dataset:
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name: CoIR Macro-Average
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type: mteb/coir
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metrics:
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- type: ndcg_at_10
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value: 0.5343
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name: NDCG@10 (meanTask)
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- task:
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type: reranking
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name: CoREB(v1)
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dataset:
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name: CoREB(v1) Macro-Average
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type: mteb/coreb
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metrics:
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- type: main_score
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value: 0.4362
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name: Main Score (
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- type: reranking_mean
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value: 0.4894
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name: Reranking Mean
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- type: map
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value: 0.4205
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name: MAP (Rank #3 Worldwide)
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- task:
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type: reranking
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name: Code-to-Text Reranking
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model-index:
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- name: code-embed-v2
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results:
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- task:
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type: reranking
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name: CoREB(v1)
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dataset:
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name: CoREB(v1) Macro-Average (Rank #3 in <=160M Class)
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type: mteb/coreb
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metrics:
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- type: main_score
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value: 0.4362
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name: Main Score (Rank #3 in <=160M Class)
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- type: reranking_mean
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value: 0.4894
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name: Reranking Mean
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- type: map
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value: 0.4205
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name: MAP (Rank #3 Worldwide)
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- task:
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type: retrieval
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name: CoIR
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dataset:
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name: CoIR Macro-Average (Rank #8 in <=141M Class)
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type: mteb/coir
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metrics:
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- type: ndcg_at_10
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value: 0.5343
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name: NDCG@10 (Rank #8 in <=141M Class)
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- task:
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type: retrieval
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name: MTEB(Code, v1)
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dataset:
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name: MTEB(Code, v1) Macro-Average (Rank #9 in <=141M Class)
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type: mteb/code
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metrics:
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- type: ndcg_at_10
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value: 0.5475
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name: NDCG@10 (Rank #9 in <=141M Class)
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- task:
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type: reranking
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name: Code-to-Text Reranking
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