kyutai/tts-voices
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You can now fine-tune embedding models in our free Unsloth notebook! 🤗
Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data.
⭐ Blog + Notebooks: https://unsloth.ai/docs/new/embedding-finetuning
Unsloth trains embedding models 1.8-3.3x faster with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups.
We'd like to thank Hugging Face and Unsloth contributor: electroglyph for making this possible!
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My Favorite Open Source Models for Jan 2026
1. General Use - https://huggingface.co/deepseek-ai/DeepSeek-V3.2
2. Reasoning - https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale
3. Coding - https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct
4. OCR - https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct
5. Image Generation - https://huggingface.co/black-forest-labs/FLUX.2-dev
6. Image Editing - https://huggingface.co/Qwen/Qwen-Image-Edit-2509
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You can now fine-tune embedding models in our free Unsloth notebook! 🤗
Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data.
⭐ Blog + Notebooks: https://unsloth.ai/docs/new/embedding-finetuning
Unsloth trains embedding models 1.8-3.3x faster with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups.
We'd like to thank Hugging Face and Unsloth contributor: electroglyph for making this possible!
Fine-tuning embedding models improves retrieval & RAG by aligning vectors to your domain-specific notion of similarity, improving search, clustering, and recommendations on your data.
⭐ Blog + Notebooks: https://unsloth.ai/docs/new/embedding-finetuning
Unsloth trains embedding models 1.8-3.3x faster with 20% less VRAM, 2x longer context & no accuracy loss vs. FA2 setups.
We'd like to thank Hugging Face and Unsloth contributor: electroglyph for making this possible!
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My Favorite Open Source Models for Jan 2026
1. General Use - deepseek-ai/DeepSeek-V3.2
2. Reasoning - deepseek-ai/DeepSeek-V3.2-Speciale
3. Coding - Qwen/Qwen3-Coder-30B-A3B-Instruct
4. OCR - Qwen/Qwen3-VL-8B-Instruct
5. Image Generation - black-forest-labs/FLUX.2-dev
6. Image Editing - Qwen/Qwen-Image-Edit-2509
What model do you use regularly?
1. General Use - deepseek-ai/DeepSeek-V3.2
2. Reasoning - deepseek-ai/DeepSeek-V3.2-Speciale
3. Coding - Qwen/Qwen3-Coder-30B-A3B-Instruct
4. OCR - Qwen/Qwen3-VL-8B-Instruct
5. Image Generation - black-forest-labs/FLUX.2-dev
6. Image Editing - Qwen/Qwen-Image-Edit-2509
What model do you use regularly?
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FunctionGemma Tuning Lab is a new no-code tool by
@google
that lets you fine-tune a model directly from the browser, with no coding knowledge required, using TRL behind the scenes.
blog: https://developers.googleblog.com/a-guide-to-fine-tuning-functiongemma/
try it out: google/functiongemma-tuning-lab
This example builds on a more advanced one for learning fine-tuning with SFT using TRL: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma
blog: https://developers.googleblog.com/a-guide-to-fine-tuning-functiongemma/
try it out: google/functiongemma-tuning-lab
This example builds on a more advanced one for learning fine-tuning with SFT using TRL: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma
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I built a crazy ultra–low latency voice assistant agent using Pipecat, NVIDIA Riva, NVIDIA NIM, and an MCP‑powered tool stack. It can talk in real time, search the web, and manage your project directory files, document your code and docs hands‑free (create, read, summarise, and clean up).
Link - https://github.com/rakshit2020/Voice-Agent-using-Nvidia-Riva-NIM-Pipecat
I put everything into a small demo repo with the full architecture diagram and a short demo video so you can see exactly how it works and adapt it to your own projects.
Check out the GitHub, play with the agent, and let me know if it’s useful or if you want a breakdown of any part of the setup.
Link - https://github.com/rakshit2020/Voice-Agent-using-Nvidia-Riva-NIM-Pipecat
I put everything into a small demo repo with the full architecture diagram and a short demo video so you can see exactly how it works and adapt it to your own projects.
Check out the GitHub, play with the agent, and let me know if it’s useful or if you want a breakdown of any part of the setup.
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