Instructions to use slyusarev/Qwen3-ASR-1.7B-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 slyusarev/Qwen3-ASR-1.7B-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 slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
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 slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
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 slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use slyusarev/Qwen3-ASR-1.7B-GGUF with Ollama:
ollama run hf.co/slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use slyusarev/Qwen3-ASR-1.7B-GGUF with Docker Model Runner:
docker model run hf.co/slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
- Lemonade
How to use slyusarev/Qwen3-ASR-1.7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull slyusarev/Qwen3-ASR-1.7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-ASR-1.7B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Qwen3-ASR-1.7B โ GGUF
GGUF builds of Qwen/Qwen3-ASR-1.7B for llama.cpp-based runtimes.
| File | Size | SHA-256 |
|---|---|---|
Qwen3-ASR-1.7B-Q4_K_M.gguf |
1.28 GB | f53b45df729c90a182612b759227daa9bb72139b012a87e558dd6c204d486ae9 |
Qwen3-ASR-1.7B-Q5_K_M.gguf |
1.26 GB | 33ca947c1a39f80f5fe0774324924dd11f603ef4c08b50aa04ead3133787e54c |
Qwen3-ASR-1.7B-Q8_0.gguf |
2.17 GB | 58e22d0532d4eacaf034cfac17a6fed159f37c41390c710186783be439d1fc57 |
mmproj-Qwen3-ASR-1.7B-Q8_0.gguf |
356 MB | 46c1d533af3f354ceb37ce855dbceff7da7fa7cf1e6a523df3b13440bd164c0d |
The mmproj file is required alongside any of the main files.
Q8_0 and mmproj are unmodified copies from ggml-org/Qwen3-ASR-1.7B-GGUF (identical SHA-256). Q4_K_M is requantized from that Q8_0.
Q5_K_M is converted from the original bf16 weights and quantized directly. The tied embedding matrix, which the converter writes twice, is stored once: output.weight is dropped and token_embd.weight kept at Q6_K, which runtimes substitute for it โ hence the file is smaller than Q4_K_M.
License: Apache 2.0, inherited from the base model.
- Downloads last month
- 172
4-bit
5-bit
8-bit
Model tree for slyusarev/Qwen3-ASR-1.7B-GGUF
Base model
Qwen/Qwen3-ASR-1.7B