- Libraries
- sentence-transformers
How to use second-state/jina-embeddings-v2-base-code-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("second-state/jina-embeddings-v2-base-code-GGUF", trust_remote_code=True)
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] - Transformers
How to use second-state/jina-embeddings-v2-base-code-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="second-state/jina-embeddings-v2-base-code-GGUF", trust_remote_code=True) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("second-state/jina-embeddings-v2-base-code-GGUF", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("second-state/jina-embeddings-v2-base-code-GGUF", trust_remote_code=True, device_map="auto") - Transformers.js
How to use second-state/jina-embeddings-v2-base-code-GGUF with Transformers.js:
// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';
// Allocate pipeline
const pipe = await pipeline('feature-extraction', 'second-state/jina-embeddings-v2-base-code-GGUF'); - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use second-state/jina-embeddings-v2-base-code-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
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 server with a web UI:
./build/bin/llama-server -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
Use Docker
docker model run hf.co/second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use second-state/jina-embeddings-v2-base-code-GGUF with Ollama:
ollama run hf.co/second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use second-state/jina-embeddings-v2-base-code-GGUF with Docker Model Runner:
docker model run hf.co/second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
- Lemonade
How to use second-state/jina-embeddings-v2-base-code-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/jina-embeddings-v2-base-code-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.jina-embeddings-v2-base-code-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Superuser666-Sigil