Sentence Similarity
GGUF
English
Chinese
multilingual
feature-extraction
embedding
text-embedding
retrieval
conversational
Instructions to use mykor/Octen-Embedding-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use mykor/Octen-Embedding-0.6B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mykor/Octen-Embedding-0.6B-GGUF", filename="Octen-Embedding-0.6B-BF16.gguf", )
llm.create_chat_completion( messages = "{\n \"source_sentence\": \"That is a happy person\",\n \"sentences\": [\n \"That is a happy dog\",\n \"That is a very happy person\",\n \"Today is a sunny day\"\n ]\n}" ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use mykor/Octen-Embedding-0.6B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mykor/Octen-Embedding-0.6B-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 mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mykor/Octen-Embedding-0.6B-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 mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mykor/Octen-Embedding-0.6B-GGUF with Ollama:
ollama run hf.co/mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
- Unsloth Studio new
How to use mykor/Octen-Embedding-0.6B-GGUF 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 mykor/Octen-Embedding-0.6B-GGUF 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 mykor/Octen-Embedding-0.6B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mykor/Octen-Embedding-0.6B-GGUF to start chatting
- Pi new
How to use mykor/Octen-Embedding-0.6B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mykor/Octen-Embedding-0.6B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use mykor/Octen-Embedding-0.6B-GGUF with Docker Model Runner:
docker model run hf.co/mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
- Lemonade
How to use mykor/Octen-Embedding-0.6B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mykor/Octen-Embedding-0.6B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Octen-Embedding-0.6B-GGUF-Q4_K_M
List all available models
lemonade list
Octen-Embedding-0.6B-GGUF
import numpy as np
import torch
from llama_cpp import Llama
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
"Octen/Octen-Embedding-0.6B",
model_kwargs={"dtype": torch.bfloat16},
)
llama = Llama.from_pretrained(
repo_id="mykor/Octen-Embedding-0.6B-GGUF",
filename="Octen-Embedding-0.6B-BF16.gguf",
verbose=False,
embedding=True,
n_ctx=0,
)
text = """์ฒ์์ ๋ฏธ์ฝํ ๋ฌผ๊ฒฐ์ด๊ฒ ์ง๋ง
๊ทธ๋ ๊ฒ ๋์ ๊ฟ์ ๋ฟ์ ์ ์๊ฒ ์ง
๊ฑฐ๋ํ ๋ฐ๋ค์ ๊ฐํ๋ฒ๋ฆฐ ๋๋ฅผ ๊ตฌํ ๋ฐฉ๋ฒ์ ์๊ณ ์์ด
์ ๋๋ก ๋ฉ์ถ์ง ๋ง, ๋น๋๊ณ ์์ผ๋
์, ์ ์ด ๋ณธ๋ค๋ฉด, ์, ํ์น๊ณ ๋ง๊ฑธ
๋ฏฟ์ง ์๋ ๊ฒ๋ค์ ๋ฌด์ํด
์ปค๋ค๋ ํ๋์ ์ผ์ผํค๋
๊ทธ ํ์ ๋๋ ๊ฐ์ง๊ณ ์์ด, ๋ด๊ฒ ๋ณด์ฌ์ค๋
ํ๋์ ์ผ์ผ์ผ ๋๋ถ์ ๋ ๋ณด์ฌ์ค
์ด ๋ฐ๋ค๋ฅผ ์ง์ด์ผํค์
ํ๋์ ์์ ํ๋์ด ์๋์ผ, ๋ฌผ๋ค์ด์ ์๋ก์
์์ผ๋ก ๋น๋๋ ๋ฐ๋ค๋ฅผ (๋ฐ๋ค๋ฅผ) ๋ง๋ค์ด ๊ฐ๋ณด์
์ฟ, ์กฐ์ฉํ ์ด๊ณณ์ ๋๊ตฐ๊ฐ ์์ด
ํ๋์ ํด๋ ํ๋ด๊ณ ์์ด
๋์น์ฑ๋ค๋ฉด, ์ผ๋ฅธ ํค์์ณ
ํด๋ฅผ ์ผํฌ ํฐ ๋ฌผ๊ฒฐ์ ๋ง๋ค์
ํ๋๋ ๋น์ ๊ฐ์ง ๋๋ง์
๋ ์์ฌํ๊ฒ ๋ ํ
์ง๋ง ๋ฏฟ์ด
๊ฐ๋ฅ์ฑ ๊ทธ๊ฑฐ๋ฉด ๋ผ
์ ์ผํ ์์ ๊ฐ์ง๊ณ ์์ผ๋
์, ์ ์ด ์์๋, ์, ๊ฐ์ง ์ ์์ด
๋ฏฟ์ง ์๋ ๊ฒ ๋ค์ ๋ฌด์ํด
์ปค๋ค๋ ํ๋์ ์ผ์ผํค๋
๊ทธ ํ์ ๋๋ ๊ฐ์ง๊ณ ์์ด, ๋ด๊ฒ ๋ณด์ฌ์ค๋
ํ๋์ ์ผ์ผ์ผ ๋๋ถ์ ๋ ๋ณด์ฌ์ค
์ด ๋ฐ๋ค๋ฅผ ์ง์ด์ผํค์
ํ๋์ ์์ ํ๋์ด ์๋์ผ, ๋ฌผ๋ค์ด์ ์๋ก์
์์ผ๋ก ๋น๋๋ ๋ฐ๋ค๋ฅผ (๋ฐ๋ค๋ฅผ) ๋ง๋ค์ด ๊ฐ๋ณด์
๋ ์ ๋ง๋ก ์ด๋ฆฌ์๊ตฌ๋ ๋ด ํ์ ๋ ๋
๋ ์ปค๋ค๋ ๋ฏธ๋๋ฅผ ๊ฟ๊ฟ ์ ์๋ค๊ณ ๋ฏฟ๋ ๋๊ฐ, ๊ทธ๋?
ํ๋์ ์ผ์ผ์ผ ์ฌ์ฐ์ ๊นจ๊ณ ๋์
์ด ๋ฐ๋ค๋ฅผ ์ง์ด์ผํค์
ํ๋์ ์์ ํ๋์ด ์๋์ผ, ๋ฌผ๋ค์ด์ ์ฐ๋ฆฌ์
์์ผ๋ก ๋น๋๋ ๋ฐ๋ค๋ฅผ (๋ฐ๋ค๋ฅผ) ๋ณด์ฌ์ฃผ๋ฌ ๊ฐ์"""
embed1 = model.encode(text)
embed2 = np.array(llama.embed(text), dtype=np.float32)
print(cos_sim(embed1, embed2).item())
0.9998425245285034
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