transformers

Prompt injection allows third-party to inject a system prompt with simple message, by inserting special HTML-like sequence (details below). Some of the issues are well-known and pretty old (almost 2 years for Transformers library) Issues: • Ollama: https://github.com/ollama/ollama/issues/15931 • HuggingFace: https://github.com/huggingface/transformers/issues/29279 (labeled as feature request) https://github.com/huggingface/transformers/issues/47822 (with a bug label) • Gemma4: https://github.com/google-deepmind/gemma/issues/768 The problem is that for tools like Ollama there is no solution except of to fix it by Ollama developers More context Why this is important: prompt injections are pretty dangerous and as long as user input is an instruction to the model it could be decided as code injections vulnerability. And in combination with lo

Anything you say back to them calling out their nonsense, they'll feed back into their LLM and it will tell them why you're wrong and they're right. https://github.com/huggingface/transformers/issues/45246

"casually dropping the most capable open weights on the planet" — @RyanMullins Google folks do something really cool! Gemma4 source: https://github.com/huggingface/transformers/pull/45192

It is a tokenizer artifact most likely (https://github.com/huggingface/transformers/issues/4786). So the output is not properly decoded in this case, it should just be a space.

This time even Unsloth could not provide bitsandbytes 4-bit models. bitsandbytes does not support new models with MoE and linear attentions, and it's much less flexible than GGUF. Nowadays I think it's better to train lora over GGUF base model, see the discussion at https://github.com/huggingface/transformers/issues/40070 I'll find some time to do this and I hope someone can do this earlier than me.

Judging by the code in the HF transformers repo[1], smaller dense versions of this model will most likely be released at some point. Hopefully, soon. [1]: https://github.com/huggingface/transformers/tree/main/src/tr...

here's the release notes: https://github.com/huggingface/transformers/releases/tag/v5....

You have to share your contact information, including DoB, and then be approved access, to obtain the models, and given that it's Meta I assume they're actually validating it against their All Humans database. They made their own DINOv3 license for this release (whereas DINOv2 used the Apache 2.0 license). Neat though. Will still check it out. As a first comment, I had to install the latest transformer==4.56.0dev (e.g. pip install git+https://github.com/huggingface/transformers) for it to work properly. 4.55.2 and earlier was failing with a missing image type in the config.

The new transformers release describes the model: https://github.com/huggingface/transformers/releases/tag/v4.... > GPT OSS is a hugely anticipated open-weights release by OpenAI, designed for powerful reasoning, agentic tasks, and versatile developer use cases. It comprises two models: a big one with 117B parameters (gpt-oss-120b), and a smaller one with 21B parameters (gpt-oss-20b). Both are mixture-of-experts (MoEs) and use a 4-bit quantization scheme (MXFP4), enabling fast inference (thanks to fewer active parameters, see details below) while keeping resource usage low. The large model fits on a single H100 GPU, while the small one runs within 16GB of memory and is perfect for consumer hardware and on-device applications.

Reference implementations are unmaintained and buggy. For example https://github.com/huggingface/transformers/issues/27961 OpenAI's tokenizer for CLIP is buggy, it's a reference implementation, it isn't the one they used for training, and the problems with it go unsolved and get copied endlessly by other projects. What about Flux? They don't say it was used for training, it wasn't, there are bugs with it that break cudagraphs or similar that aren't that impactful. On the other hand, it uses CLIP reference, and CLIP reference is buggy, so this is buggy...

They are still deterministic. You can set temperature to zero to get the output to be consistent, but even the temperature usually uses a seed or psuedo random number generator. Though this would depend on the implementation. https://github.com/huggingface/transformers/blob/d538293f62f...