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Hey folks, I'm on the Gemma team, we released new model(s) just recently, and I saw many questions here about function calling. We just published the docs to de

Hey folks, I'm on the Gemma team, we released new model(s) just recently, and I saw many questions here about function calling. We just published the docs to detail this more. In short Gemma3's prompted instruction following is quite good for the larger models and that's how you use the feature. You don't need to take our word for it! We were waiting for an external and independent validation from the Berkeley team, and they just published their results. You can use their metrics to get a rough sense of performance, and of course try it out yourself in AIstudio or locally with your own prompts. https://gorilla.cs.berkeley.edu/leaderboard.html https://gorilla.cs.berkeley.edu/leaderboard.html Hope you all enjoy the models!

brianjking 2y ago
Thanks, Gemma is fantastic and that it supports function calling is great.
attentive 2y ago
ToolACE-2-8B and watt-tool-8B have impressive score for the size in that leaderboard.
chadash 2y ago
so if i'm reading this correctly, it's essentially prompt engineering here and there's no guarantee for the output. Why not enforce a guaranteed output structure by restricting the allowed logits at each step (e.g. what outlines library does)?
- canyon289 2y ago
  So in short there's no guarantee for any output from any LLM whether its Gemma or any other (ignoring some details like setting a random seed or parameters like temperature to 0). Like you mentioned though libraries like outlines can constrain the output, whereas hosted models often already include this in their API, but they can do so because its a model + some server side code. With Gemma, or any open model, you can use the open libraries in conjunction to get what you want. Some inference frameworks like Ollama include structured output as part of their functionality. But you mentioned all of this already in your question so I feel like I'm missing something. Let me know! But I think you already mentioned all this in your response so I might be missing the question?
  - programmarchy 2y ago
    With OpenAI models, my understanding is that token output is restricted so that each next token must conform to the specified grammar (ie json schema) so you’re guaranteed to get either a function call or an error. Edit: per simonw’s sibling comment, ollama also has this feature.
    - canyon289 2y ago
      Ah, There's a distinction here with model vs model framework. The ollama inference framework supports token output restriction. Gemma in AI Studio also does, as does Gemini, there's a toggle in the right hand panel, but that's because both those models are being served in the API where the functionality is present in the server. The Gemma model by itself does not though, nor does any "raw" model, but many open libraries exist for you to plug into whatever local framework you decide to use.
- simonw 2y ago
  If you run Gemma via Ollama (as recommended in the Gemma docs) you get exactly that feature, because Ollama provides that for any model that they run for you: https://ollama.com/blog/structured-outputs https://ollama.com/blog/structured-outputs Under the hood, it is using the llama.cpp grammars mechanism that restricts allowed logits at each step, similar to Outlines.
  - refulgentis 2y ago
    I've been working on tool calling in llama.cpp for Phi-4 and have a client that can switch between local models and remote for agentic work/search/etc., I learned a lot about this situation recently: - We can constrain the output of a JSON grammar (old school llama.cpp) - We can format inputs to make sure it matches the model format. - Both of these combined is what llama.cpp does, via @ochafik, in inter alia, https://github.com/ggml-org/llama.cpp/pull/9639 https://github.com/ggml-org/llama.cpp/pull/9639. - ollama isn't plugged into this system AFAIK To OP's question, specifying a format in the model unlocks training the model specifically had on functions calling: what I sometimes call an "agentic loop", i.e. we're dramatically increasing the odds we're singing in the right tune for the model to do the right thing in this situation.
    - anon373839 2y ago
      Do you have thoughts on the code-style agents recommended by huggingface? The pitch for them is compelling, since structuring complex tasks in code is something very natural for LLMs. But then, I don’t see as much about this approach outside of HF.
jampekka 2y ago
Is the format used in the examples the same that's used in the function calling instruction training, i.e. should it be the optimal prompt for function calling? I find it a bit frustrating when details of the training is not known and one has to guess what kinds of prompts the model has been tuned with.
- canyon289 2y ago
  We feel this model excels at instructability which is why we're recommending bringing your own prompt! Benchmark wise you can see this performance from BFCL directly, they (independently) ran their eval using their prompted format the larger Gemma models performed quite well if you ask me. Specifically though I want to thank you for leaving a comment. We're reading all this feedback and its informing what we can do next to reduce frustration and create the best model experience the community
  - jampekka 2y ago
    Do you mean that the exact prompt for tool use shouldn't matter? Has this been tested? Is the tool use trained with a variety of prompt styles? I would imagine training with a specific, perhaps structured, prompt could make the function calling a bit more robust.
    - canyon289 2y ago
      Ah I see where confusion might come. I don't mean the exact prompt shouldn't matter, but I am saying that we noticed that these series of models picked up on tool call format quite readily in our various tests, which is what we express in the docs. We tested internally and I hope the independent BFCL results speak for themselves! All their code and evals are public fully public. > I would imagine training with a specific, perhaps structured, prompt could make the function calling a bit more robust. This is absolutely true. I show this in a tutorial last year where Gemma2 is finetuned for a specific format, and with some targeted SFT it produces a json output more readily. https://www.youtube.com/watch?v=YxhzozLH1Dk https://www.youtube.com/watch?v=YxhzozLH1Dk So this is all to say, Gemma is designed to be a great model for multiple types of users. If you want to use the "out of the box" weights with your own format, go ahead! We hope that makes it easier to integrate with whatever tooling you're using with minimal headache. If you need specific performance on your bespoke format finetune the model to be your own! Finetuning is supported across many frameworks so pick whatever library you like best. This is all to say we hope Gemma is flexible and usable for folks like yourself along a variety of dimensions. For myself I'm learning there's big interest in a specific prompt. Again can't thank you enough for the feedback here.
BoredPositron 2y ago
Don’t wanna be that guy but you guys have too many models. I love Gemini and Gemma but it’s way too crowded atm.
deleted 2y ago
[deleted]

brianjking 2y ago

Thanks, Gemma is fantastic and that it supports function calling is great.

attentive 2y ago

ToolACE-2-8B and watt-tool-8B have impressive score for the size in that leaderboard.

chadash 2y ago

so if i'm reading this correctly, it's essentially prompt engineering here and there's no guarantee for the output. Why not enforce a guaranteed output structure by restricting the allowed logits at each step (e.g. what outlines library does)?

canyon289 2y ago
So in short there's no guarantee for any output from any LLM whether its Gemma or any other (ignoring some details like setting a random seed or parameters like temperature to 0). Like you mentioned though libraries like outlines can constrain the output, whereas hosted models often already include this in their API, but they can do so because its a model + some server side code. With Gemma, or any open model, you can use the open libraries in conjunction to get what you want. Some inference frameworks like Ollama include structured output as part of their functionality. But you mentioned all of this already in your question so I feel like I'm missing something. Let me know! But I think you already mentioned all this in your response so I might be missing the question?
- programmarchy 2y ago
  With OpenAI models, my understanding is that token output is restricted so that each next token must conform to the specified grammar (ie json schema) so you’re guaranteed to get either a function call or an error. Edit: per simonw’s sibling comment, ollama also has this feature.
  - canyon289 2y ago
    Ah, There's a distinction here with model vs model framework. The ollama inference framework supports token output restriction. Gemma in AI Studio also does, as does Gemini, there's a toggle in the right hand panel, but that's because both those models are being served in the API where the functionality is present in the server. The Gemma model by itself does not though, nor does any "raw" model, but many open libraries exist for you to plug into whatever local framework you decide to use.
simonw 2y ago
If you run Gemma via Ollama (as recommended in the Gemma docs) you get exactly that feature, because Ollama provides that for any model that they run for you: https://ollama.com/blog/structured-outputs https://ollama.com/blog/structured-outputs Under the hood, it is using the llama.cpp grammars mechanism that restricts allowed logits at each step, similar to Outlines.
- refulgentis 2y ago
  I've been working on tool calling in llama.cpp for Phi-4 and have a client that can switch between local models and remote for agentic work/search/etc., I learned a lot about this situation recently: - We can constrain the output of a JSON grammar (old school llama.cpp) - We can format inputs to make sure it matches the model format. - Both of these combined is what llama.cpp does, via @ochafik, in inter alia, https://github.com/ggml-org/llama.cpp/pull/9639 https://github.com/ggml-org/llama.cpp/pull/9639. - ollama isn't plugged into this system AFAIK To OP's question, specifying a format in the model unlocks training the model specifically had on functions calling: what I sometimes call an "agentic loop", i.e. we're dramatically increasing the odds we're singing in the right tune for the model to do the right thing in this situation.
  - anon373839 2y ago
    Do you have thoughts on the code-style agents recommended by huggingface? The pitch for them is compelling, since structuring complex tasks in code is something very natural for LLMs. But then, I don’t see as much about this approach outside of HF.

jampekka 2y ago

Is the format used in the examples the same that's used in the function calling instruction training, i.e. should it be the optimal prompt for function calling? I find it a bit frustrating when details of the training is not known and one has to guess what kinds of prompts the model has been tuned with.

canyon289 2y ago
We feel this model excels at instructability which is why we're recommending bringing your own prompt! Benchmark wise you can see this performance from BFCL directly, they (independently) ran their eval using their prompted format the larger Gemma models performed quite well if you ask me. Specifically though I want to thank you for leaving a comment. We're reading all this feedback and its informing what we can do next to reduce frustration and create the best model experience the community
- jampekka 2y ago
  Do you mean that the exact prompt for tool use shouldn't matter? Has this been tested? Is the tool use trained with a variety of prompt styles? I would imagine training with a specific, perhaps structured, prompt could make the function calling a bit more robust.
  - canyon289 2y ago
    Ah I see where confusion might come. I don't mean the exact prompt shouldn't matter, but I am saying that we noticed that these series of models picked up on tool call format quite readily in our various tests, which is what we express in the docs. We tested internally and I hope the independent BFCL results speak for themselves! All their code and evals are public fully public. > I would imagine training with a specific, perhaps structured, prompt could make the function calling a bit more robust. This is absolutely true. I show this in a tutorial last year where Gemma2 is finetuned for a specific format, and with some targeted SFT it produces a json output more readily. https://www.youtube.com/watch?v=YxhzozLH1Dk https://www.youtube.com/watch?v=YxhzozLH1Dk So this is all to say, Gemma is designed to be a great model for multiple types of users. If you want to use the "out of the box" weights with your own format, go ahead! We hope that makes it easier to integrate with whatever tooling you're using with minimal headache. If you need specific performance on your bespoke format finetune the model to be your own! Finetuning is supported across many frameworks so pick whatever library you like best. This is all to say we hope Gemma is flexible and usable for folks like yourself along a variety of dimensions. For myself I'm learning there's big interest in a specific prompt. Again can't thank you enough for the feedback here.

BoredPositron 2y ago

Don’t wanna be that guy but you guys have too many models. I love Gemini and Gemma but it’s way too crowded atm.

deleted 2y ago

[deleted]