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
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf divakantpandey/code-embed-v2:Q8_0 --embeddings
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible embeddings server: ./build/bin/llama-server -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
Update code-embed-v2: Fast Lane fine-tuned weights (+21.52% macro gain across 17 MTEB benchmarks)
d7759ca verified Download code_embed_config.json from divakantpandey/code-embed-v2: direct link, hf CLI and curl.
- Browser
- Download file 212 Bytes
-
https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/code_embed_config.json
- Command line
-
hf download hf://divakantpandey/code-embed-v2/code_embed_config.json
-
curl -L -o code_embed_config.json https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/code_embed_config.json
212 Bytes
| { | |
| "base_model_name_or_path": "microsoft/unixcoder-base", | |
| "backbone_hidden_size": 768, | |
| "embedding_dim": 1536, | |
| "projection_hidden_dim": 1536, | |
| "pooling": "cls", | |
| "max_seq_length": 8192, | |
| "dropout": 0.1 | |
| } |