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docs: fix typos, remove unnecessary text, and refine README wording

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kantan-kanto
2026-03-14 09:32:58 +09:00
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@@ -19,7 +19,7 @@ Based on extensive testing, **Wan2.2** and **Qwen-Image-Edit** respond **signifi
Starting from **v1.0.8**, image input for **Qwen2.5-VL** is now available in **version 0.3.16 of llama-cpp-python(official)**.
Vision input support varies by model and llama-cpp-python version. See Installation section for detailed compatibility information. Results may vary based on your specific environment.
**Recommendation:** Use **Qwen3-VL** with Qwen-Image-Edit. **Qwen2.5-VL** currently shows insufficient adherence to user prompts under the existing system prompt configuration.
**Recommendation:** Use **Qwen API** or **Qwen3-VL** with Qwen-Image-Edit. **Qwen2.5-VL** currently shows insufficient adherence to user prompts under the existing system prompt configuration.
### Local GGUF Model Stability
Starting from **v1.0.6**, internal GGUF model handling has been improved to ensure stable behavior
@@ -242,14 +242,9 @@ Add your Alibaba Cloud Dashscope API key to this file.
- ❌ Insufficient adherence to user prompts under the existing system prompt configuration with **Qwen-Image-Edit**
### Qwen3-VL (Separate mmproj)
- ✅ Qwen3-VL(4B/7B): Full vision support with JamePeng fork
- ✅ Qwen3-VL(4B/8B): Full vision support with JamePeng fork
- ✅ Requires matching mmproj file
### Recommended Quantization
- **Q4_K_M**: Balanced quality/size (recommended for most users)
- **Q5_K_M**: Higher quality, larger size
- **Q8_0**: Maximum quality, largest size
### Model Sources
- Qwen models: https://huggingface.co/Qwen
- GGUF conversions: https://huggingface.co/models?search=qwen+gguf
@@ -257,40 +252,12 @@ Add your Alibaba Cloud Dashscope API key to this file.
---
## Configuration
### System Requirements
- **RAM**: 8GB+ (16GB recommended for 7B models)
- **Storage**: 3-8GB per model (depending on quantization)
- **GPU**: Optional (CPU execution supported)
- NVIDIA GPU: CUDA support via llama-cpp-python
- AMD GPU: ROCm support (requires specific build)
- Intel Arc: Limited support, CPU recommended
### Performance Tips
1. **Use Q4_K_M quantization** for faster inference and lower memory usage
2. **Reduce max_tokens** if hitting memory limits
3. **Enable GPU** if you have compatible hardware (select `GPU` in device dropdown)
4. **Use CPU for stability** if encountering GPU issues
5. **Batch multiple requests** when possible for efficiency
6. **Close other applications** to free up RAM during inference
### Memory Usage Guide
| Model | Quantization | RAM Usage |
|-------|--------------|-----------|
| Qwen3-VL-4B | Q4_K_M | ~4-5GB |
| Qwen3-VL-4B | Q8_0 | ~7-8GB |
| Qwen3-VL-7B | Q4_K_M | ~6-7GB |
| Qwen3-VL-7B | Q8_0 | ~12-14GB |
---
## Troubleshooting
### Installation Issues
**Q: "No module named 'llama_cpp'" error**
A: Install llama-cpp-python: `pip install llama-cpp-python==0.3.21 --break-system-packages`
A: Install llama-cpp-python: `pip install llama-cpp-python`
**Q: pip install fails with "externally-managed-environment"**
A: Use `--break-system-packages` flag or create a virtual environment