Instructions to use batiai/Qwen3-Embedding-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use batiai/Qwen3-Embedding-0.6B-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 batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

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 batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

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 batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

Use Docker

docker model run hf.co/batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use batiai/Qwen3-Embedding-0.6B-GGUF with Ollama:
```
ollama run hf.co/batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K
```
Unsloth Desktop

How to use batiai/Qwen3-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 serve -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use batiai/Qwen3-Embedding-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K
```

Lemonade

How to use batiai/Qwen3-Embedding-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

Run and chat with the model

lemonade run user.Qwen3-Embedding-0.6B-GGUF-Q6_K

List all available models

lemonade list

Hermes Agent

How to use batiai/Qwen3-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 serve -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

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 batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use batiai/Qwen3-Embedding-0.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "batiai/Qwen3-Embedding-0.6B-GGUF:Q6_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

hero775 commited on Apr 19

Commit

d84b43e

verified ·

1 Parent(s): 25ddd54

Swap default to Q6_K — drift measured indistinguishable, saves disk on Mac

Browse files

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -46,8 +46,8 @@ library_name: llama.cpp
 ### Ollama (one command)
 ```bash
-ollama pull batiai/qwen3-embedding:0.6b        # 610 MB (Q8_0 default)
-ollama pull batiai/qwen3-embedding:0.6b-q6     # 472 MB (smaller alt)
 # Use via Ollama embeddings API
 curl http://localhost:11434/api/embeddings -d '{
@@ -77,10 +77,12 @@ curl http://localhost:8080/v1/embeddings \
 | File | Quant | Size | When to use |
 |------|-------|------|-------------|
-| `Qwen3-Embedding-0.6B-Q6_K.gguf` | Q6_K | **472 MB** | smaller footprint, same quality envelope |
-| **`Qwen3-Embedding-0.6B-Q8_0.gguf`** | **Q8_0** | **610 MB** | **default — near-lossless, recommended** |
-Why no IQ3 / IQ4 for embedding? Unlike chat LLMs, embedding quality cascades into cosine-similarity drift at low bit-widths — every query after is affected. At this model size **Q8_0 is the safe default**, Q6_K is a smaller-disk alternative that stays within 0.01 average drift.
 ## Quality Verification (measured)

 ### Ollama (one command)
 ```bash
+ollama pull batiai/qwen3-embedding:0.6b        # 472 MB (Q6_K default — recommended)
+ollama pull batiai/qwen3-embedding:0.6b-q8     # 610 MB (Q8_0 — max quality)
 # Use via Ollama embeddings API
 curl http://localhost:11434/api/embeddings -d '{
 | File | Quant | Size | When to use |
 |------|-------|------|-------------|
+| **`Qwen3-Embedding-0.6B-Q6_K.gguf`** | **Q6_K** | **472 MB** | **recommended default — we measured drift vs Q8 at cos 0.997 (indistinguishable on retrieval)** |
+| `Qwen3-Embedding-0.6B-Q8_0.gguf` | Q8_0 | 610 MB | maximum quality, ~25 % bigger disk |
+Why Q6 over Q8 as default? On our 4-stage harness the two are functionally equivalent — Q6 actually *edged out* Q8 by 2.5 pp on real-doc top-1 recall (measurement noise, but confirms Q6 is not inferior). 150 MB savings matters on 8 GB Macs. If you want maximum conservatism, pull `:0.6b-q8`.
+Why no IQ3 / IQ4 for embedding? Unlike chat LLMs, embedding quality cascades into cosine-similarity drift at low bit-widths — every query is affected. Q6_K / Q8_0 are the safe range.
 ## Quality Verification (measured)