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Reuben fernandes PRO

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LLM

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published a model about 8 hours ago
Reubencf/konkani-llama3.1-8b-instruct
reacted to seyf1elislam's post with 🔥 about 19 hours ago
# 🚀 Run Qwen3-TTS on Colab GPU or Locally Run **Qwen3-TTS (Text-to-Speech & Voice Cloning)** with minimal effort. This setup is based on the official HF Space. ### 🔗 Links * **Official Space:** https://huggingface.co/spaces/Qwen/Qwen3-TTS * **GitHub Repo:** https://github.com/seyf1elislam/qwen-tts-webui-notebook * **Colab:** https://github.com/seyf1elislam/qwen-tts-webui-notebook/blob/main/Qwen_TTS_(TTS_%26_Voice_Cloning)_Colab.ipynb --- ### 📓 Method 1: Google Colab (Fastest) 1. Open the https://github.com/seyf1elislam/qwen-tts-webui-notebook/blob/main/Qwen_TTS_(TTS_%26_Voice_Cloning)_Colab.ipynb. 2. Add your HF_TOKEN to Google Colab Secrets 3. Ensure you are on a **T4 GPU** runtime. 4. Run all cells. Use the `gradio.live` link to open the UI. --- ### 💻 Method 2: Local Installation Requires an GPU. Uses `uv` for faster setup. ```bash # 1. Install uv & Clone pip install uv git clone https://huggingface.co/spaces/Qwen/Qwen3-TTS && cd Qwen3-TTS # 2. Setup Environment uv venv uv pip install -r requirements.txt # 3. Auth & Run uvx hf auth login python app.py # UI available at: http://localhost:7860/ ```
reacted to kostakoff's post with 👍 about 19 hours ago
I created list of models based on permissive license (apache2, mit, openrail) and raw fp16 weights. LLM: - Mistral 7b v1 - Falcon 7b - GLM4 9b - Olmo3 7b - Yi 9b - Qwen3 8b - Internlm3 8B - PHI4 Multimodal LLM: - Pixtral 12b - Qwen3-VL-8B-Instruct Picture generation: - Stable Diffusion 1.5 - Stable Diffusion 2.0 - Stable Diffusion XL Video generation: - WAN 2.1 VACE Diffusers TTS: - SUNO Bark This can be very useful for those who are just starting their AI LLM journey in PyTorch, like me. Suggestions in the comments are welcome.
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