Instructions to use Legenddefsx/paralel-tr-en-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Legenddefsx/paralel-tr-en-embedder with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Legenddefsx/paralel-tr-en-embedder")

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 Legenddefsx/paralel-tr-en-embedder 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 Legenddefsx/paralel-tr-en-embedder
# Run inference directly in the terminal:
llama cli -hf Legenddefsx/paralel-tr-en-embedder

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Legenddefsx/paralel-tr-en-embedder
# Run inference directly in the terminal:
llama cli -hf Legenddefsx/paralel-tr-en-embedder

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 Legenddefsx/paralel-tr-en-embedder
# Run inference directly in the terminal:
./llama-cli -hf Legenddefsx/paralel-tr-en-embedder

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 Legenddefsx/paralel-tr-en-embedder
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Legenddefsx/paralel-tr-en-embedder

Use Docker

docker model run hf.co/Legenddefsx/paralel-tr-en-embedder

LM Studio
Jan
Ollama
How to use Legenddefsx/paralel-tr-en-embedder with Ollama:
```
ollama run hf.co/Legenddefsx/paralel-tr-en-embedder
```

Unsloth Studio

How to use Legenddefsx/paralel-tr-en-embedder with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Legenddefsx/paralel-tr-en-embedder to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Legenddefsx/paralel-tr-en-embedder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Legenddefsx/paralel-tr-en-embedder to start chatting

Docker Model Runner
How to use Legenddefsx/paralel-tr-en-embedder with Docker Model Runner:
```
docker model run hf.co/Legenddefsx/paralel-tr-en-embedder
```

Lemonade

How to use Legenddefsx/paralel-tr-en-embedder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Legenddefsx/paralel-tr-en-embedder

Run and chat with the model

lemonade run user.paralel-tr-en-embedder-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

paralel-tr-en-embedder / 1_Pooling /config.json

Legenddefsx

Upload fine-tuned model

0dcdb4b verified 9 days ago

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	{
	"embedding_dimension": 384,
	"pooling_mode": "cls",
	"include_prompt": true
	}