Sentence Similarity
GGUF
sentence-transformers
quantized
embedding
Qwen3
imatrix
llama-cpp
feature-extraction
Instructions to use PeterAM4/Qwen3-Embedding-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PeterAM4/Qwen3-Embedding-0.6B-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PeterAM4/Qwen3-Embedding-0.6B-GGUF") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PeterAM4/Qwen3-Embedding-0.6B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M --embeddings
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M --embeddings
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 embeddings server: ./build/bin/llama-server -hf PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M --embeddings
- LM Studio
- Jan
- Ollama
How to use PeterAM4/Qwen3-Embedding-0.6B-GGUF with Ollama:
ollama run hf.co/PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use PeterAM4/Qwen3-Embedding-0.6B-GGUF with Docker Model Runner:
docker model run hf.co/PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M
- Lemonade
How to use PeterAM4/Qwen3-Embedding-0.6B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PeterAM4/Qwen3-Embedding-0.6B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Embedding-0.6B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download benchmark_results.csv from PeterAM4/Qwen3-Embedding-0.6B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 927 Bytes
-
https://huggingface.co/PeterAM4/Qwen3-Embedding-0.6B-GGUF/resolve/main/benchmark_results.csv
- Command line
-
hf download hf://PeterAM4/Qwen3-Embedding-0.6B-GGUF/benchmark_results.csv
-
curl -L -o benchmark_results.csv https://huggingface.co/PeterAM4/Qwen3-Embedding-0.6B-GGUF/resolve/main/benchmark_results.csv
927 Bytes
| Model,Size,BPW,Perplexity | |
| BF16-baseline,1.1G,16.08,406.0250 | |
| Q8_0,610M,8.58,409.5689 | |
| Q6_K,472M,6.65,417.3712 | |
| Q5_1,442M,6.23,426.9407 | |
| Q5_K_M,424M,5.96,442.9431 | |
| Q5_0,416M,5.86,413.1916 | |
| Q5_K_S,416M,5.86,414.9329 | |
| Q4_1-imat,390M,5.49,403.0646 | |
| Q4_K_M-imat,378M,5.32,406.6788 | |
| Q4_K_S-imat,365M,5.14,406.9947 | |
| Q4_0-imat,364M,5.13,419.8843 | |
| IQ4_NL-imat,364M,5.12,435.0203 | |
| Q3_K_L-imat,351M,4.94,412.0217 | |
| IQ4_XS-imat,351M,4.94,451.4025 | |
| Q3_K_M-imat,331M,4.66,406.6408 | |
| IQ3_M-imat,320M,4.51,460.9405 | |
| IQ3_S-imat,308M,4.34,475.4797 | |
| Q3_K_S-imat,308M,4.34,340.2907 | |
| IQ3_XS-imat,298M,4.20,520.3907 | |
| Q2_K-imat,282M,3.97,797.8549 | |
| Q2_K_S-imat,267M,3.76,1561.2449 | |
| IQ3_XXS-imat,266M,3.74,613.9329 | |
| IQ2_M-imat,252M,3.55,1283.4407 | |
| IQ2_S-imat,242M,3.41,1857.4142 | |
| TQ2_0-imat,236M,3.32,16937766.5110 | |
| IQ2_XS-imat,231M,3.25,3632.9250 | |
| IQ2_XXS-imat,219M,3.08,5641.8950 | |
| TQ1_0-imat,216M,3.04,16937766.5110 | |
| IQ1_M-imat,206M,2.90,7495.4178 | |
| IQ1_S-imat,198M,2.79,23414.9432 | |