Sentence Similarity
sentence-transformers
ONNX
Safetensors
GGUF
English
qwen3
feature-extraction
graphql
retrieval
embeddings
text-embeddings-inference
Instructions to use xthor/Qwen3-Embedding-0.6B-GraphQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use xthor/Qwen3-Embedding-0.6B-GraphQL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("xthor/Qwen3-Embedding-0.6B-GraphQL") 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 xthor/Qwen3-Embedding-0.6B-GraphQL with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf xthor/Qwen3-Embedding-0.6B-GraphQL:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf xthor/Qwen3-Embedding-0.6B-GraphQL: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 xthor/Qwen3-Embedding-0.6B-GraphQL: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 xthor/Qwen3-Embedding-0.6B-GraphQL:Q4_K_M --embeddings
- LM Studio
- Jan
- Ollama
How to use xthor/Qwen3-Embedding-0.6B-GraphQL with Ollama:
ollama run hf.co/xthor/Qwen3-Embedding-0.6B-GraphQL:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use xthor/Qwen3-Embedding-0.6B-GraphQL with Docker Model Runner:
docker model run hf.co/xthor/Qwen3-Embedding-0.6B-GraphQL:Q4_K_M
- Lemonade
How to use xthor/Qwen3-Embedding-0.6B-GraphQL with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull xthor/Qwen3-Embedding-0.6B-GraphQL:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Embedding-0.6B-GraphQL-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from xthor/Qwen3-Embedding-0.6B-GraphQL: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/xthor/Qwen3-Embedding-0.6B-GraphQL/resolve/main/tokenizer.json
- Command line
-
hf download hf://xthor/Qwen3-Embedding-0.6B-GraphQL/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/xthor/Qwen3-Embedding-0.6B-GraphQL/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- e316f183d8aed282dc0e2439c7907309a7ab91dcf8da2f3a8283fbf39b59b19a
- Size of remote file:
- 11.4 MB
- SHA256:
- ff513191adbac19eecd59d6f7465b3a8504446facfff8978e24fe6eda45968c8
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