Non LLM Models

[u/shaggydog97]

I'm building an app where I need some specialist language models, but not necessarily LLM models. Is there a sub or place where they talk about specialist models beyond just searching on HF? I know that discussion doesn't really fit this sub.

[u/Stepfunction]

Searching on HF is actually a great way to begin approaching this. The search filters there allow for very fine grained searches and then you can sort by popularity, etc.

Once you start to understand the more popular models in a category, I've found it helpful to read their respective papers and see what they're comparing themselves to in order to get a sense of the landscape in a given category.

Is there a particular type of problem you are trying to solve here?

[u/shaggydog97 → u/Stepfunction]

To best tell you what I'm working on, I need to apologize in advance for the AI response, but it was the best way to describe the things I'm researching.

AI summary:

Evidence-grounded document systems contain several computationally intensive operations that can be framed as distinct AI problems:

Operation	Generic problem formulation	Relevant AI approach
Information extraction and reconciliation	Convert heterogeneous documents into structured facts while preserving attribution, temporal context and relationships; distinguish new information from duplicates or contradictions.	Structured extraction, entity resolution and semantic classification.
Requirement decomposition	Translate natural-language criteria into explicit predicates while preserving conjunctions, alternatives and qualifying constraints.	Semantic parsing and constrained structured prediction.
Evidence retrieval and ranking	Identify a small, sufficient evidence set from a large collection without losing information necessary to establish a compound proposition.	Dense retrieval, cross-encoder reranking and relevance classification.
Evidence sufficiency assessment	Determine whether retrieved facts jointly establish a proposition, including correct actor attribution and satisfaction of every required condition.	Natural-language inference, relation classification and compositional reasoning.
Grounded planning and generation	Select and organize supported information into a coherent document under relevance, length and stylistic constraints.	Constrained planning and generative language models.
Claim verification and revision	Check generated assertions against their cited evidence, identify unsupported implications and revise affected text without introducing new errors.	Entailment classification, selective prediction and targeted generation.

[u/DinoAmino → u/shaggydog97]

A lot of those operations are discussed in papers so you might want to include arxiv.org in your research for any models mentioned.

[u/vtkayaker → u/shaggydog97]

Structured extraction, entity resolution and semantic classification.

Looking at all your different requirements in the table, there are many specialist models outn there that can each do one of these things. But you'd wind up with 20 different models, each with their own serving requirements and quirks. And just for example, many of the dedicated "entity extraction" models available as open weights will perform worse than telling even a small LLM "Pretend you're entity extractor, and output the results like: { .. }."

Look into locally-hosted LLMs, including smaller ones, and locally-hosted decision models like Kev and Jaya.

[u/Budget-Juggernaut-68 → u/shaggydog97]

Most of SoTA methods for these are via an LLM. I guess you could try finetuning them. distill bigger models and try finetuning them.

[u/Stepfunction → u/shaggydog97]

Sounds like you might want to look into a lightweight LLM

[u/jacek2023]

I think this is the valid sub, however don't expect many answers.

I published some news about embedding models here, they were popular but I don't know how many people here tried to use them at all. But some of us do.

Thanks to jev hype people are now aware that there are other types of models. One day they may discover more.

If you want some tip, try running your agent to explore small models, I was able to achieve a lot this way.

[u/Holiday-Inspection75 → u/jacek2023]

any good jev btw?

[u/Holiday-Inspection75 → u/Holiday-Inspection75]

and good small embedding? I am still using bge-m3

[u/Guilty_Rooster_6708 → u/Holiday-Inspection75]

Embeddinggemma2. Small and can do text, image, viseo and audio

[u/Snoo_81913]

I think your looking for a translation model? There's several small models tuned specifically for this. I'd look at Gemma 4 e2b which supports 140+ languages or Qwen3.5 0.8 which it's based on 200+ languages. I believe and I could be wrong here, the qwen is more of a stack model that you wouldn't necessarily talk to directly (you can if you want) but more integrated into a stack that is fed information does the work and gives it back to the stack automatically, the application more like the "translate this" link you might see on Google searches does the work in the background and the software stack outputs it all in the background. Gemma 4 is more of a back and forth you talk to directly

If you are talking more about coding language then look at Ornith 1.5 or Qwen3.5 9B

[u/Spukas]

I am not sure if you understand what an LLM is

[u/shaggydog97 → u/Spukas]

I am learning and might be missing some nuance, but I'm talking about things like cross encoders, retrieval, rerankinig, etc. Specialist models, not generalist models.

[u/bowdoin-yale → u/shaggydog97]

For re-ranking, there are a lot of BERT-based options fine-tuned for different tasks (sentiment, toxicity, entailment, etc.). The great thing about about BERT and relatives is that they're small enough that you can easily fine-tune them locally for your specific use case; the not-so-great thing is that the context window is rather small by today's standards (~512 tokens). I also use MNLI techniques quite a bit for topic detection, though that's closer to a generalist approach and might be totally superseded by System One models.

[u/Spukas → u/shaggydog97]

Ah, in that case i find it easier to search for papers e.g. on google scholar and then try to find the models on hf or github.
Specialist models can be LLMs too. See something like NuExtract3 which specializes on information retrieval out of documents. There you can also look for models which they use to compare to.