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eb426ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # SPITITOUT Hugging Face Space
This version runs without Gemini or any external model API. The React frontend calls a FastAPI backend inside the same Hugging Face Space.
## Recommended models
- Text on CPU: `Qwen/Qwen3-1.7B-GGUF`
- Served through `llama-cpp-python` using the official `Qwen3-1.7B-Q8_0.gguf` quantized file.
- Text on GPU: `Qwen/Qwen3-4B-Instruct-2507`
- Use `LLM_BACKEND=transformers` for simple GPU deployment, or add vLLM as a separate server for higher throughput.
- Speech to text: `openai/whisper-tiny`
- Small and multilingual. Use `openai/whisper-base` if accuracy is more important than latency.
- Text to speech: `hexgrad/Kokoro-82M` via `kokoro`
- 82M parameters, lightweight, Apache licensed, and supports Mandarin voices such as `zf_xiaobei`.
## Space settings
Create the Space as a Docker Space, then push this folder.
Suggested environment variables:
```bash
LLM_BACKEND=llamacpp
GGUF_MODEL_REPO=Qwen/Qwen3-1.7B-GGUF
GGUF_MODEL_FILE=Qwen3-1.7B-Q8_0.gguf
LLAMA_CPP_N_CTX=4096
ASR_MODEL=openai/whisper-tiny
KOKORO_LANG_CODE=z
KOKORO_VOICE=zf_xiaobei
MAX_NEW_TOKENS=220
```
For CPU-only testing:
```bash
LLM_BACKEND=llamacpp
GGUF_MODEL_REPO=Qwen/Qwen3-1.7B-GGUF
GGUF_MODEL_FILE=Qwen3-1.7B-Q8_0.gguf
ASR_MODEL=openai/whisper-tiny
MAX_NEW_TOKENS=140
```
## Local run
```bash
npm install
npm run build
pip install -r requirements.txt
python app.py
```
Then open `http://localhost:7860`.
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