Instructions to use rodraco/Qwen3-Embedding-4B-F16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use rodraco/Qwen3-Embedding-4B-F16-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 rodraco/Qwen3-Embedding-4B-F16-GGUF:F16 # Run inference directly in the terminal: llama cli -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16 # Run inference directly in the terminal: llama cli -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
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 rodraco/Qwen3-Embedding-4B-F16-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
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 rodraco/Qwen3-Embedding-4B-F16-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
Use Docker
docker model run hf.co/rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use rodraco/Qwen3-Embedding-4B-F16-GGUF with Ollama:
ollama run hf.co/rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
- Unsloth Desktop
- Pi
How to use rodraco/Qwen3-Embedding-4B-F16-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
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": "rodraco/Qwen3-Embedding-4B-F16-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use rodraco/Qwen3-Embedding-4B-F16-GGUF with Docker Model Runner:
docker model run hf.co/rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
- Lemonade
How to use rodraco/Qwen3-Embedding-4B-F16-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
Run and chat with the model
lemonade run user.Qwen3-Embedding-4B-F16-GGUF-F16
List all available models
lemonade list
- Hermes Agent
How to use rodraco/Qwen3-Embedding-4B-F16-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 rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
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 rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use rodraco/Qwen3-Embedding-4B-F16-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rodraco/Qwen3-Embedding-4B-F16-GGUF:F16
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 "rodraco/Qwen3-Embedding-4B-F16-GGUF:F16" \ --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"
Qwen3-Embedding-4B β F16 GGUF (provenance-documented conversion)
An F16 GGUF conversion of Qwen/Qwen3-Embedding-4B for serving with llama-server, published with a complete, verifiable provenance chain β source revision, converter version, and output checksum.
Why this repo exists
Qwen3-Embedding GGUFs have a known conversion-vintage trap: llama.cpp's Qwen3-Embedding conversion fixes (EOS/sep handling, pooling metadata β definitive fix #15023, 2025-08-02) live in convert_hf_to_gguf.py, so a GGUF converted before the fix has the bug baked in permanently β a current runtime cannot repair it (see #14234). Many third-party conversions have squashed histories, making their conversion vintage unprovable.
This conversion is documented at commit level so you don't have to trust β you can check:
| Provenance fact | Value |
|---|---|
| Source repo | Qwen/Qwen3-Embedding-4B (official, Apache-2.0) |
| Source revision | 5cf2132abc99cad020ac570b19d031efec650f2b |
| Source shard sha256 | model-00001β¦: e70bfe3c970523fb7ef4eddffed2254ce3f1e7150c3de2af4342de129dd756f8 Β· model-00002β¦: ed1b87c8e9eb7e535a1a155e4fd00d9f4dba80e58a6db48a4c9f82cede7079c1 (verified pre-conversion) |
| Converter | convert_hf_to_gguf.py from the official ghcr.io/ggml-org/llama.cpp:full-cuda13 image, digest sha256:500f241b5d7e27846bdda69ce6f4889db8de859ea3988d4265e5d9d7b7f1079b β post-#15023 |
| Conversion date | 2026-07-05 |
| Output | Qwen3-Embedding-4B-F16.gguf, 8,049,889,760 bytes, sha256 aae9f588cb47cba4503d5a0c71f64c90ade6a207dff2ff874972e2db6b099d35 |
| Pooling metadata | qwen3.pooling_type = 3 (LAST) present in the GGUF header β the #15023 fix's marker |
The exact conversion command:
docker run --rm -v "$PWD/src:/src:ro" -v "$PWD/out:/out" \
ghcr.io/ggml-org/llama.cpp:full-cuda13@sha256:500f241b5d7e27846bdda69ce6f4889db8de859ea3988d4265e5d9d7b7f1079b \
--convert /src --outtype f16 --outfile /out/Qwen3-Embedding-4B-F16.gguf
Fidelity check
Verified against the official safetensors pipeline (sentence-transformers, same revision): cosine(reference, served) over a 66-probe multilingual set (Romanian, three diacritic-variant forms) β min cosine 0.99976, i.e. the served GGUF endpoint reproduces the reference embeddings.
Serving
llama-server -m Qwen3-Embedding-4B-F16.gguf --embeddings --pooling last -ngl 999 \
--ctx-size 8192 --ubatch-size 2048
--pooling lastmust be explicit. Qwen3-Embedding is a causal decoder β CLS/mean pooling produces silently wrong embeddings. Bring-up check: the verbose server log should printpooling type = 3, and any returned vector should have L2 norm β 1.0 (normalization is server-side by default).- Never combine
--embeddingswith--rerankingon one instance (llama.cpp #20085: all-zero embeddings). - VRAM guidance: F16 weights are ~8.0 GiB; on a 16 GiB card use
--ubatch-size 2048(a 8192 ubatch allocates a ~4.9 GiB compute buffer and OOMs). Ubatch is throughput-only for causal decoders β it never changes embedding values.
Query instruction protocol (client-side)
Per the official model card, queries should be instruction-formatted while documents are embedded plain:
Instruct: {task_description}
Query:{query}
The server embeds whatever text it receives β applying the instruction prefix is the caller's job. Skipping it typically costs 1β5% retrieval quality.
License & attribution
Apache-2.0, same as the base model. All model weights are Β© the Qwen team (Qwen/Qwen3-Embedding-4B); this repo contributes only the format conversion and its provenance documentation.
- Downloads last month
- 27
16-bit