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
Periodic checkpoint at step 2250 (loss=0.00000)
Browse files- backbone/model.safetensors +1 -1
- optimizer.pt +1 -1
- projection_head.pt +1 -1
- rng_state.pt +1 -1
- scheduler.pt +1 -1
- sentence_transformers/2_Dense/model.safetensors +1 -1
- sentence_transformers/3_Dense/model.safetensors +1 -1
- sentence_transformers/README.md +3 -3
- sentence_transformers/model.safetensors +1 -1
- trainer_state.json +2 -2
backbone/model.safetensors
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sentence_transformers/2_Dense/model.safetensors
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sentence_transformers/3_Dense/model.safetensors
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sentence_transformers/README.md
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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# tensor([[1.0000, 0.
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# [0.
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# [0.
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```
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<!--
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### Direct Usage (Transformers)
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities)
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+
# tensor([[1.0000, 0.4935, 0.3333],
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# [0.4935, 1.0000, 0.3925],
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# [0.3333, 0.3925, 1.0000]])
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```
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<!--
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### Direct Usage (Transformers)
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sentence_transformers/model.safetensors
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trainer_state.json
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"current_epoch": 0,
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"total_training_steps": 6314,
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"scheduler_name": "cosine",
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"global_step": 2250,
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"learning_rate": 2.4382877991412166e-05,
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"scheduler_name": "cosine",
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"fp16": false,
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"bf16": true
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