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skkut/qwen3-embedding-0.6b-q4f16_1-MLC
qwen3-embedding-0.6b-q4f16_1-MLC is a feature extraction model from skkut. Use it when you need embeddings to search or compare text. The card lists the license as apache-2.0.
MLC-compiled Qwen3-Embedding-0.6B (Apache-2.0) in web-llm 0.2.84's format for WebGPU in-browser embedding. Built locally with mlc-llm (tvmjs 0.24.0-dev3 / mlc-ai 0.26.dev246 generation) + emsdk 3.1.56.
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Updated Aug 22, 2026
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From the Hugging Face model README
MLC-compiled Qwen3-Embedding-0.6B (Apache-2.0) in web-llm 0.2.84's format for WebGPU in-browser embedding. Built locally with mlc-llm (tvmjs 0.24.0-dev3 / mlc-ai 0.26.dev246 generation) + emsdk 3.1.56.
requestDevice shim is needed (unlike the 8B build)Build note: unlike the 8B checkpoint (which declares tie_word_embeddings=false
but ships no lm_head.weight), the 0.6B is genuinely tied — the tie patch is a
no-op here.
| File | Purpose |
|---|---|
mlc-chat-config.json | model config (ctx 2048, chunk 2048, max_batch 2) |
tensor-cache.json | weight manifest |
params_shard_0.bin … params_shard_8.bin | quantized weights (~335 MB) |
tokenizer.json, tokenizer_config.json, vocab.json, merges.txt | Qwen3 tokenizer |
qwen3-embedding-0.6b-q4f16_1-ctx2048_batch2-webgpu.wasm | the model library (wasm) |
const record = {
model: 'https://huggingface.co/skkut/qwen3-embedding-0.6b-q4f16_1-MLC/resolve/main/',
model_id: 'Qwen3-Embedding-0.6B-q4f16_1-MLC',
model_lib: 'https://huggingface.co/skkut/qwen3-embedding-0.6b-q4f16_1-MLC/resolve/main/qwen3-embedding-0.6b-q4f16_1-ctx2048_batch2-webgpu.wasm',
model_type: 1, // EmbeddingModelType
vram_required_MB: 1500,
};
Query-side texts should use the Qwen3 instruction prefix
(Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: …).
On the 0.6B the prefix's absolute cosine gain is smaller than on the 4B/8B
(typical family guidance: 1–5%) — smoke-verified retrieval ranking stays correct
across a mixed EN/ZH probe set either way.