Instructions to use tex8/Octen-Embedding-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tex8/Octen-Embedding-8B-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("tex8/Octen-Embedding-8B-GGUF")

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]

llama-cpp-python

How to use tex8/Octen-Embedding-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tex8/Octen-Embedding-8B-GGUF",
	filename="Octen-Embedding-8B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tex8/Octen-Embedding-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tex8/Octen-Embedding-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tex8/Octen-Embedding-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tex8/Octen-Embedding-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use tex8/Octen-Embedding-8B-GGUF with Ollama:
```
ollama run hf.co/tex8/Octen-Embedding-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tex8/Octen-Embedding-8B-GGUF to start chatting

Pi new

How to use tex8/Octen-Embedding-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tex8/Octen-Embedding-8B-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": "tex8/Octen-Embedding-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-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 tex8/Octen-Embedding-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use tex8/Octen-Embedding-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/tex8/Octen-Embedding-8B-GGUF:Q4_K_M
```

Lemonade

How to use tex8/Octen-Embedding-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tex8/Octen-Embedding-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Octen-Embedding-8B-GGUF-Q4_K_M

List all available models

lemonade list

Octen-Embedding-8B - GGUF Quantizations

GGUF quantizations of Octen/Octen-Embedding-8B, converted using llama.cpp b8110.

Octen-Embedding-8B is a fine-tune of Qwen/Qwen3-Embedding-8B, ranked #1 on the RTEB Leaderboard.

Quantized by tex8 — a platform building AI-native web solutions and cloud services.

Available Quantizations

File	Quant	Size	Description
`Octen-Embedding-8B-Q4_K_M.gguf`	Q4_K_M	4.0 GB	Good balance of size and quality
`Octen-Embedding-8B-Q6_K.gguf`	Q6_K	6.5 GB	High quality, moderate size
`Octen-Embedding-8B-Q8_0.gguf`	Q8_0	8.0 GB	Near-lossless, recommended

All quantizations were created with --leave-output-tensor and --token-embedding-type F16 to preserve embedding quality.

Usage with llama.cpp

llama-embedding \
  -m Octen-Embedding-8B-Q8_0.gguf \
  --pooling last \
  -p "Your text here"

Usage with llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="Octen-Embedding-8B-Q8_0.gguf",
    embedding=True,
    n_gpu_layers=-1,
    n_ctx=2048,
)

result = llm.create_embedding("Your text here")
embedding = result['data'][0]['embedding']  # 4096-dim vector

Conversion Command

# Step 1: Convert to F16
python convert_hf_to_gguf.py Octen/Octen-Embedding-8B \
  --outfile Octen-Embedding-8B-f16.gguf \
  --outtype f16

# Step 2: Quantize
llama-quantize \
  --leave-output-tensor \
  --token-embedding-type F16 \
  Octen-Embedding-8B-f16.gguf \
  Octen-Embedding-8B-Q8_0.gguf Q8_0

Downloads last month: 60

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for tex8/Octen-Embedding-8B-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-Embedding-8B

Finetuned

Octen/Octen-Embedding-8B

Quantized

(8)

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