Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
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Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
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Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#2I 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.
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#3If 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/
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#4If you are interested in small language model to fine tune, gemma3:270m is quite interesting for its size
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#5If 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 pr…
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.
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#6If 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
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#7> 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)
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#8> 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).
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#9existing 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.
Re: Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
#10If 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 pr…
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