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
Update code-embed-v2: Fast Lane fine-tuned weights (+21.52% macro gain across 17 MTEB benchmarks)
d7759ca verified Download config.json from divakantpandey/code-embed-v2: direct link, hf CLI and curl.
- Browser
- Download file 772 Bytes
-
https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/config.json
- Command line
-
hf download hf://divakantpandey/code-embed-v2/config.json
-
curl -L -o config.json https://huggingface.co/divakantpandey/code-embed-v2/resolve/main/config.json
772 Bytes
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "RobertaModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 1026, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.20.2", | |
| "type_vocab_size": 10, | |
| "use_cache": false, | |
| "vocab_size": 51416 | |
| } | |