What are some important small/tiny local models to download that aren't the main chatbot LLMs, but are things for like audio, TTS, STT, vision, or whatever random important miscellaneous things like that which might be useful to get before the bans and shutdowns come in?
Most of us already know the main important mainline local LLMs to save like Qwen3.6 27b, Gemma4 31b, GLM5.2, etc. And then for diffusion models, Z-Image Turbo, Flux Klein, LTX2.3, and Wan2.2. But, what about those random little special use case models. I…
Most of us already know the main important mainline local LLMs to save like Qwen3.6 27b, Gemma4 31b, GLM5.2, etc. And then for diffusion models, Z-Image Turbo, Flux Klein, LTX2.3, and Wan2.2. But, what about those random little special use case models. I don't know anything about those and almost never see people talk about them on here when I browse, but see them mentioned like once every few months to know they even exist, but don't know which ones to get or which ones are important for what purposes. Like, what Audio or STT or TTS or vision or whatever else types of models do I need for special use cases that those are useful for (i.e. models that enable you to be able to chat voice-to-voice or whatever other format to format stuff with, or other random things like this, that you need these little enabler models for)?
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Embedding and rerankers for sure. Lots of decent-to-good options too. https://huggingface.co/nvidia/Nemotron-3-Embed-1B-BF16 https://huggingface.co/google/embeddinggemma-300m https://huggingface.co/Qwen/Qwen3-Embedding-8B https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B https://huggingface.co/jinaai/jina-embeddings-v5-omni-small https://huggingface.co/microsoft/bitnet-embedding-0.6b https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B https://huggingface.co/tencent/R3-rerank-0.6b https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2 (and so on..)
Thanks! (also thanks u/glass_wheel and u/jc2046 for your recommendations as well). Btw, I know I can just look it up, but, in case a lot of others are lurking and browsing, if you guys can explain the use cases/importance of these maybe it would be helpful since not everyone knows what these do (I've been on here for 6 months and don't know what these ones do, and there is some chance that I'm not the only noob on this entire sub, :p
Think of it (kind of) as a filter and sort mechanism for things like RAG / agent memory that works both as input and output - you add items to the filter by creating embeddings, then later retrieve those by comparing a query vector to the existing vectors in the list of vectors, and then you sort them using a reranker to make the list better. Basically, the embedding model takes language [and sometimes other modalities like images] (your chat histories, documents, etc - whatever you've decided to index) and turns it into a bunch of vectors (an array of numbers) and then stores that vector with a value that allows it to associate that to the original language location / text / whatever. Then, when you search your memory system / perform a RAG search, your query gets converted into a vector by the same embedding model and then it filters down to say the top 20 most closely related (mathematically) items to your query in the vector database. But getting 20 mathematically similar results kinda sucks because vector search is a bit imprecise, so then you put those results through a reranker model that looks at the original text in the query and the 20 initial results and gives them a score between 0 and 1 typically and then the highest score wins. So you can then reduce the results down to the top ~3 or so that are most likely to be the most relevant and best answer to your original query. Embed --> create vector index Search --> create vector for query & compare to all vectors in index and give the most closely matched. Rerank --> take text from query and compare to search results and sort for best fit / relevance.
Give Parakeet v2 (for English) and v3 (for multi-language) a shot instead of Whisper or Qwen3 ASR. I found Parakeet v2 wildly better for English ASR, like 5x in terms of speed and much better accuracy.
https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b is worth a look too.. bit different target, but if that's your target.. it's pretty solid.
The recently released TabFM is pretty cool. A 350M foundation model for tabular data like spreadsheets/dataframes is nifty, and it's small enough that pretty much anyone can just download it and check it out.
Will do! I'm always up for free performance boosts!
I've grabbed copies of the DINOv2 and DINOv3 models and SAM models. You can also get them from modelscope if you don't want to give your info to Meta. The "gate" for downloading them from HF is pretty loose though. DINOv2 and v3 are excellent segmentation models of various sizes, which can be used for further downstream tasks. They are super fast, so good for robotics or training video/video game models. SAM is more "out-of-the-box" useful. I really like that the DINO models are competent while being in the millions of parameters vs billions. https://huggingface.co/facebook/dinov2-base https://huggingface.co/facebook/dinov2-small https://huggingface.co/facebook/dinov2-large https://huggingface.co/facebook/dinov2-giant (and lots more variations) https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m https://huggingface.co/facebook/dinov3-vit7b16-pretrain-lvd1689m (again, many variants) https://huggingface.co/facebook/sam3.1 https://huggingface.co/Comfy-Org/sam3.1 There might be better voice models than Microsoft's vibe voice now, I don't know, I haven't kept up, but it was one of the best voice cloners for a while. They took down the 7B one from HF, but it survives via other repos. https://huggingface.co/vibevoice/VibeVoice-7B Geneformer (human cell transcriptomes) and Evo 2 ( general DNA) will probably be useless to 99.999999% of people, but this is exactly the kind of thing Anthropic says that you shouldn't be able to have. https://arcinstitute.org/tools/evo https://huggingface.co/arcinstitute/evo2_40b_base If you have a lot of storage/compute, I would consider getting the Olmo training dataset, just so you have the fundamental base of information to train a model, it might be useful in the future. For those who are paranoid, like I am, it's nice to have a dataset that can be used to give a nice little pass over a local model, just to nudge its weights to be something different than off-the-shelf, and you can insert your own brain-washing policy into a model. https://huggingface.co/collections/allenai/olmo-3-pre-training https://huggingface.co/collections/allenai/olmo-3-post-training
check your ~/.cache/huggingface folder to see what random little models the apps you use have pulled down. You'll see things like whisper, segmentation models etc in there. back those up / download them to wherever you're archiving all this.
kokoro tts
Omnivoice is my current favorite for voice cloning. Whisper's an obvious one for asr but I've come to prefer qwen 3 asr. For diarization I like FoxNoseTech. MOSS-Transcribe-Diarize is an interesting one but it hasn't really been as good as a qwen/fox combo for me. Still good enough to keep around though. For vision and translation I use gemma. The important point there is to have 'all' the gemma sizes. Which I use is heavily dependent on the task. E2B can be surprisingly good given how small it is. I've had fairly good experiences using it to translate Japanese on the fly in games.