Gemma 3 270M re-implemented in pure PyTorch for local tinkering
github.com207 points by ModelForge 5 hours ago
207 points by ModelForge 5 hours ago
Hey all, I created this model with a top notch team. I answered many questions last week when this hit the front page, and happy to answer more here as well.
https://news.ycombinator.com/item?id=44902148
Personally I'm excited that you all have access to this model now and hope you all get value out of using them.
Very stupid question: why does the tflite model output only '[multimodal][multimodal]' when executed on GPU in the AI edge gallery app, while fully working on the CPU.
I would like to know your thoughts on using 2/3 of such a small the model's size for embeddings. What would be different if you used a byte-level vocabulary and spent the parameter budget on transformer parameters instead? I think you would lose performance (tok/s) but might gain accuracy.
At this small scale the embeddings indeed were a big focus. Consider this thought process.
The tokens themselves are a form of compression. Lets say we have the word "WaffleHouse", character level this would be 11 tokens, but with an embedder this would be perhaps 2 or 3 tokens (I didn't actually run through the tokenizer but we could verify precisely). This matters a lot for on device processing especially.
So while we could get more intelligence out of the model by bumping up the "knowledge" parameters, the device would need to process more input and output tokens.
Another advantage on small devices is the embeddings are just a lookup table which requires little to no computation. Its the rest of the parameters that have the expensive matrix multplications, so if we increased those we'd also be increasing the number of FLOPs needed for a forward pass.
This blog post explains it well. https://www.adamcasson.com/posts/transformer-flops
So all this to say is there are definite tradeoffs between model size, performance on evals, and compute cost. We ran many internal experiments with different choices to see could work well, and then picked what we believed work will best for the open community.
How would this matrix get trained with PyTorch? I currently have a toy Transformer network - I ended up marking the matrix as sparse and using SparseAdam - gives a bit of a performance boost, but at the same time I can't use torch.compile() on the fetch from this matrix.
Thanks for your work, it is really an amazing small LM.
Can you share what kind of hardware is necessary to train it, and how long it took?
Thank you!
The Gemma3 technical report contains many details on training setup https://arxiv.org/pdf/2503.19786
This was released with the initial batch of Gemma3 so it doesn't contain the 270m details, nonetheless you'll get a good idea of what it takes to build these models.
Does it have function calls? Can we use it with MCP?
It can possibly perform basic prompted FC but I wouldn't get your hopes up. It should be to be a solild FC model if trained on specific tools and format. I would not expect great MCP performance because the context window is 32k and most MCP servers I've see implicitly assume massive context windows.
I imagine you and your team have finetuned the model on different tasks, can you share some results? (I have only seen the alien NPC finetuning)
The Unsloth folks have finetuning numbers. Linking their post here https://www.reddit.com/r/unsloth/comments/1mq5hbb/google_gem...
This might be a very basic question, but as a dev whose only interaction with models is using the main commercial ones (sonnet, ChatGPT and the like), what are some usecases for these smaller local models?
What usages can be reasonable to expect from them? Are there uses out of the box or does one have to go through some custom post-training to get useful behavior?
I feel like there is a huge gap between understanding models as a user of commercial tools and the kind of discussions happening in these threads, but I’m not sure what are the in-between steps.
Summarization, very basic tool use, without needing to go across the internet and back, and zero cost because of edge compute.
Its a crucial question. I wrote up a long answer here. Let me know it helps
Can someone (or OP) point me to a recipe to fine tune a model like this for natural language tasks like complicated NER or similar workflows? I tried finetuning Gemma3 270M when it came out last week without any success. A lot of tutorials are geared towards chat applications and role playing but I feel this model could be great for usecases like mine where I am trying to extract clean up and extract data from PDFs with entity identification and such.
Have you tried this one here by any chance?
https://huggingface.co/dslim/bert-base-NER
Just wondering if it’s worth testing and what it would be most useful for.
What use-cases do you see for the 270M’s embeddings, and should we be sticking to token embeddings or can we meaningfully pool for sentence/document embeddings?
Do we need to fine-tune for the embeddings to be meaningful at the sentence/document level?
That’s wild that with a KV cache and compilation on the Mac CPU you are faster than on an A100 GPU.
Because on Mac the CPU and GPU share memory, but A100 need to transfer to RAM/CPU on the parts that’s not supported by GPU?
(My first guess)
This would be because the GPU can’t fill its waveform and hide memory latency, no? I’m curious for a reason why
If you wanted to train it from scratch, how long would it take on a reasonable GPU setup?
For the sake of comparison, you can train a 124M model on a 3090 (see nanoGPT). In that case, each batch ends up having about 500,000 tokens and takes maybe around 10ish seconds to run forward and backward. Then the 6 trillion tokens that this model was trained on would take about 4 years, approximately. Or just "too long" for a shorter answer.
The world reasonable is vague but assuming you mean something that could be run in a residential unit it would long a very long time if training from pure scratch.
This is part of the rationale for releasing this model. Now you don't have to start from scratch and finetuning is reasonable on a wide variety of hardware, including reasonable GPU setups (and smaller)
Do you think these very small models have some utility in the real world? Apart from learning and academic purposes of course.
Yes! To me the primary value is not just as a teaching or toy model. I see a lot o value in repeatable tasks if we think about enterprise and a local fast developer model for individual usage.
Here's some examples that are inspired by previous roles I had outside of Google, where a business I was working in needed real time text processing.
This tutorials were made with Gemma versions from a year ago, but could now be recreated with Gemma 270m
https://developers.googleblog.com/en/gemma-for-streaming-ml-... https://www.youtube.com/watch?v=YxhzozLH1Dk
It seems to be more often correct than wrong for multilingual translation tasks(source text from[1][2]). Rough, but probably useful as traveler's phrase books.
You
Translate to English: Рошешуа́р (фр. Rochechouart) — місто, муніципалітет у Франції, у регіоні Нова Аквітанія, департамент Верхня В'єнна. Населення — 3637 осіб (2022)[1]. Муніципалітет розташований на відстані[2] близько 360 км на південь від Парижа, 34 км на захід від Ліможа.
gemma-3-270m-it-qat
Here's the translation:
"Rochechouart is a city in France, a municipality located in the region of New Aeterna, department within the Upper Vienne Province. The population is 3637 people (2022). The city is situated at a distance of approximately 360 kilometers from Paris, 34 km from Lyon.
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Translate to English: Bộ Công Thương đề xuất hỗ trợ hộ gia đình lắp điện mặt trời mái nhà, có hệ thống lưu trữ tối đa 2-3 triệu đồng, hỗ trợ lãi suất vay trong tối đa ba năm. Nội dung được Bộ Công Thương đưa ra tại dự thảo lần 3 Quyết định của Thủ tướng về chính sách hỗ trợ cho hộ gia đình lắp đặt điện mặt trời mái nhà tự sản, tự tiêu và hệ thống lưu trữ điện.
gemma-3-270m-it-qat
The Ministry of Industry and Trade proposes supporting households with solar panels in their homes, with a maximum savings of 2-3 million đồng, providing loan assistance for up to 3 years. The content of the Ministry's decision at the Third Review of the Government's Policy on Support for Households Installing Solar Panels in Homes, approved by the Prime Minister about the policy support for households installing solar panels themselves, and the system storage capacity is maximum 2-3 million đồng.
2: https://vnexpress.net/lap-dien-mat-troi-mai-nha-tu-dung-co-t...
Sure, interacting with natural language without expectation that the model contains knowledge. Good for things like tool use and embeddings where the information is all retrieved.
Are these small models are trained to privilege "raw intelligence" over factual knowledge? Is there any indication of how much of current model is dedicated to the knowledge of multiple languages and tons of facts rather than pure understanding and reasoning?
The evaluations provide this indication. You'll see MMLU, GPQA, Big Bench etc in reports for many models. Those numbers provide the indication you're looking for.
To answer a question you didn't ask. With small models especially we need to make choices as to which to focus on. For this model we focused on text summarization and instruction following, with the idea that users would finetune to gain performance on the task set that is relevant to them
what a legend
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