If you are going to go to the bother of fine tuning for trivial problems like subject classification then I think you'll find Scikit Learn with a SGDClassifier on 2-grams will do probably just as well and be under 1MB for the trained classifier.

You can train it in under a minute, and it will work perfectly well on embedded devices.

Small LLMs are good choices for text classification in two cases:

- If you next to provide in-context examples and classifier based on them.

- Your classification goes beyond simple subject-type classifiers. For example, multiple choice question answering is classification where small LLM will work but traditional ML methods won't/

Not with 800 examples. If you are going to consider an ngram model, I think you are better off getting a frontier llm to write you an absurd regex.

A small transformer like BERT or variants is a better fit. It only takes a few examples, which can be generated synthetically using an LLM.

Trains quickly and classifies speedily on modern hardware.

Had a lot of fun doing stuff like this years ago, before LLMs were a thing.

Used BERT-based classifiers for exactly this a few years back. Main gotcha: tokenizer overhead dominates at low latency, not the forward pass. Batch requests or you're wasting most of your inference budget on padding. Worth it once you hit real traffic though.

there are models between 2-grams and 600m param models that would be good options. i don't expect a 2-gram to do very well here. also i'm not sure why this model isn't a fine choice if it solves their problem

Yeah we hit this exact wall. Ended up using DistilBERT for a ticket classifier instead of full-size models, way faster to fine-tune and still nailed accuracy above 95%. Qwen 0.6B is total overkill for pure categorization but if it's already running locally for other stuff, no reason not to reuse it.

If you want to go deeper on language models, try these project ideas:

- Zero-shot encoders like tasksource or GliNER

- Natural language inference: https://huggingface.co/blog/dleemiller/nli-xenc-ways-to-use

- GRPO training

- GEPA prompt tuning Qwen 0.6B (or GEPA, then GRPO)

- Use an embedding model and train a classifier (MLP, logistic, svm)

- Use a larger LLM to generate a synthetic dataset (beware of lack of diversity, mine "seed text" from real sources first)

- Synthetically generate "hard examples" where more than one category may be valid and DPO tune your preferred responses

may I ask where did you get the list? I am looking for ways to get involved in going little more deeper on LLMs (I have very high level understanding, but my direct work doesn't involve them, hence I am not familiar with deeper details)

If you are interested in small language model to fine tune, gemma3:270m is quite interesting for its size

> The model invents new categories (e.g. apartments) and doesn’t stick to the provided list of allowed categories

Can this specific failure mode be solved by providing a grammar that the output must adhere to? (Not sure if Qwen has this feature, it's used for eg. to ensure the output is parseable json)

It can.

It's something that is implemented by the thing that runs the model - eg Llama.cpp - rather than the model itself.

Note that it is hard to make work if you turn thinking on because the grammar gets complicated quickly (I don't recall if Qwen 0.6B can do thinking).

Thinking shouldn't be too hard to deal with---just let the model generate freely until it hits a </think> token, then do constrained decoding, right?

Yes, you can use constrained decoding like logit masking to force all invalid tokens in the vocabulary to -inf, and effectively be removed from selection. I believe llama.cpp exposes this by accepting a formatted grammar.

This was my thought as well. I'm surprised that it's not being used here (afaict)

But why using an encoder model instead of a BERT based model? For a pure classification that should be easier to train and work quite well

Has anyone compared recently doing something like ModernBERT plus classifier vs. full or lora FT of a small LM like qwen?

I have! I recently compared Gemma 1b to ModernBERT Large for a binary classification task and ModernBERT was the clear winner. It learned faster and performed the task better by a significant margin by the end of training. It seems the bidirectional encoder only architecture works really well for classification tasks, and I think it is related to being bidirectional whereas decoder only models like Gemma (or Qwen) can only “look backwards”. I used a mixture of FFT and LoRA as well as a mixture of CE Loss and SupCon Loss.

Nobody FTs a classifier and load-tests it before it hits prod. Watch that Qwen setup eat your GPU memory and latency budget the first time traffic spikes. ModernBERT's boring and small. Boring wins at 3am.

“As an example, the question “When did we replace our pool pump?” will be mapped to a category called “pool” before querying the Index database.”

Cool write up! Really appreciate it but incidentally how does this categorization help you get better retrieval results?

Categorization allows for retrieval strategy

In a general sense, you see this crop up in SKILLS.md files and other places, as LLMs have to deal with broad contexts. People try and drill into some taxonomy in a naive fashion using plaintext as a directive, which is not particularly optimal.

I wonder if one could build a 'mixture of experts' at the model level that leveraged a variety of small models "within" a larger model...

because you have a different vector store for each categorization?

What if the question crosses categories?

existing embedding models like alibaba's modernbert tune or one of the jina v5s would probably map query to category automatically. (i.e. store embeddings of each category and calculate cosine sim for each incoming query vs. categories and pick the closest)

also, you could stick a classifier head on a BERT model as another option.

Tangentially related, but the UK Gov Incubator for AI has quite a nifty LLM driven classification pipeline for survey answers.

https://github.com/i-dot-ai/consult

Isn’t a cosine similarity over the text embeddings a much more effective way to handle categorization?

The whole reason why embeddings work so well is because they encode the underlying meaning of the texts

Do small language models run on cpus or you still need a gpus to run them?

Anything below one billion parameters you can run on the CPU at acceptable speed

For larger sizes you still can, it just becomes slower and slower. For a simple classification task (small input, tiny output, and you can constrain output to a couple tokens) you could even run something like a 4B or 8B model on the CPU

I guess that technically depends on the software used to run the model, but in general it's always been possible to run on a CPU (and may even be possible to run on TPU or something else). It's just been slower. Likewise GPU RAM vs system RAM and the bandwidths involved can make hard bottlenecks.

GPU and VRAM (or fast unified RAM) is generally the option that is both available and performant, but especially really small models also run quite well on CPU and system RAM.

Ran a classifier like this on a $5/mo VPS with no GPU, CPU only. Fine for our use case since it's async - user doesn't need output in 200ms, just needs it before the next email batch goes out. Saved us from paying for a bigger box we didn't need.

iGPUs are often slower or only as fast as CPUs when it comes to LLM text generation.

The advantage is mainly in memory bandwidth. External GPUs' internal memory is slightly faster than DDR attached to your CPU.

Other types of "AI" models do make use of the extra compute in GPUs but not LLMs.

Are 0.6b models useful without fine tuning?

Half of the times I ask qwen 0.6b "what is 1 + 2?" it ends up in a thinking loop of "but wait, the user is asking me to ..."

If you don't want the thinking, you can pass `enable_thinking: false` to the `chat_template_kwargs`. If using promptfoo, this can be done via:

    providers:
      - # llama-server
        id: openai:chat:qwen
        config:
          apiBaseUrl: http://localhost:7876
          apiKey: "..."
          passthrough:
            chat_template_kwargs:
              enable_thinking: false

The looping may be due to quantization -- I've seen it on locally quantized Q6_K Qwen 3.5/3.6 models. I recall seeing somewhere (here or r/LocalLlama) that Qwen models are sensitive to quantization of the keys, though I haven't yet experimented with/looked into fixing this. (I've been building up my promptfoo tests/infrastructure to detect looping, etc. on Qwen and other models.)

A fun thing I do with Qwen 3.5 0.8b is to take a screenshot of the Hackernews homepage and ask it to give me a JSON representation of the data and it does surprisingly well. With a well structured prompt I think it could be made to be pretty reliable tool for that type of task out of the box.

While a fun poc, surely it would be better to just use the API (see the footer)? Or just `curl | x2j | jq` and map the HTML directly to JSON?

If you're gonna fine-tune for a closed set classification problem like this, you could just fine-tune BERT and get a faster model with better performance.

I think the Qwen 0.6B is so cool. It is super fast and as illustrated here it has a clear niche, esp. when fine-tuned.

I'm also interested in it as a student for distillation.

I mean it's always nice to play around with sLLM finetuning, but for practical purposes I would always start with a lazy learner using embeddings (something like a small Stella model), pre-embed the topics/categories, embed the question, perform a kNN using cosine distance. You can use an LLM to "expand" the topics before embedding to make them more contextual. This is usually super fast and super simple and gives you a nice baseline. Then I would add a classification head after embedding layer (with maybe some dropout + 2-3 MLP layers) and train my own classifier, and compare that to lazy learner. Only after that would I start finetuning an LLM.

Very cool write-up and GitHub repo!

Is it just me or half these comments read like AI

0.6B model to sort questions into buckets. Regex had good run.