Instructions to use AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp 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 AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp:BF16 --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp:BF16 --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp:BF16 --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp with Ollama:
ollama run hf.co/AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp:BF16
- Unsloth Desktop
- Docker Model Runner
How to use AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp with Docker Model Runner:
docker model run hf.co/AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp:BF16
- Lemonade
How to use AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AXERA-TECH/Qwen3-Embedding-0.6B-AX8860-llama.cpp:BF16
Run and chat with the model
lemonade run user.Qwen3-Embedding-0.6B-AX8860-llama.cpp-BF16
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Qwen3-Embedding-0.6B on AX8860 (llama.cpp + ggml-axera, 2026-10-31)
在爱芯 AX8860 的 NPU(4 核)上用 llama.cpp 运行 Qwen3-Embedding-0.6B(文本向量,1024 维)。 Qwen3-Embedding-0.6B (text embeddings, 1024 dims) on the 4-core NPU of the Axera AX8860, with llama.cpp.
文件 / Files
| 文件 / File | 说明 / Description |
|---|---|
qwen3-emb-0.6b-bf16.gguf |
模型权重(GGUF BF16,1.1 GB,md5 896a1a1dd90d26cb1800c0e1c7bbe12c)/ weights |
bin/ |
llama.cpp 板端程序 llama-embedding、llama-server(静态链接,只依赖系统 /opt/lib 与 libc)/ the board programs (static; need only the system /opt/lib and libc) |
kernels/ |
NPU kernel:整个模型一个(28 层在 kernel 内循环)+ manifest.json / one kernel for the whole model + manifest.json |
run.sh |
运行脚本 / run script |
权重只来自 GGUF:kernel 不含权重,加载时从 GGUF 转成 MXFP8。 The weights come only from the GGUF: the kernel holds none; they are converted to MXFP8 at load.
运行 / Run
环境 / Requirements:AX8860,SDK V0.5.0 或更新(cat /proc/ax_proc/version)/ SDK V0.5.0 or newer.
./run.sh "今天天气很好" # 一条文本的向量(JSON,已归一化)/ one text (JSON, normalized)
./run.sh -f texts.txt # 每行一条 / one embedding per line
./run.sh -s 8080 # OpenAI 兼容服务 / OpenAI-compatible server
curl http://<board>:8080/v1/embeddings -H 'Content-Type: application/json' -d '{"input":"今天天气很好"}'
直接调用程序时设置 / When calling the programs directly: LD_LIBRARY_PATH=/opt/lib GGML_AXERA_KERNEL_DIR=<repo>/kernels,
-ngl 99,-c 最大 4096;服务端 --embedding --pooling last -np 1。检索时 query 按模型卡加指令前缀
(Instruct: <task>\nQuery: <query>) / for retrieval, prefix queries as the model card says.
性能 / Performance
AX8860(DDR 7500 MT/s),SDK V0.5.1,NPU 4 核 / 4 cores:
| 项 / Item | 结果 / Result |
|---|---|
| 服务模式单条 query(9 token)/ one query on the server | 22 ms |
| 服务模式单条 229 token / one 229-token text on the server | 36 ms |
| 884 token(2 条)/ 884 tokens (2 texts) | 69 ms |
正确性 / Correctness
与上一版 NPU 实现(已验证与 CPU 检索结果一致)的向量余弦 0.9991–0.9997。 Cosine 0.9991-0.9997 to the vectors of the previous NPU implementation (whose retrieval results matched the CPU's).
已知限制 / Known limits
- 运行时独占 NPU 4 核 / takes all 4 NPU cores; do not run other NPU programs alongside.
- 上下文最大 4096 token / context up to 4096 tokens.
源码 / Sources
- llama.cpp:ZHEQIUSHUI/llama.cpp
feat/qwen3-whole@19737a5(ggml/src/ggml-axera/models/qwen3) - kernel:ZHEQIUSHUI/ax-dsl-kernels
main@67e8473(models/qwen3_embedding)
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