These implementations are largely reference implementations for educational purposes and are not expected to be run in production. If you use model.generate directly, you need to apply the harmony format manually using the chat template or use our openai-harmony package. Both models were trained using our harmony response format and should only be used with this format; otherwise, they will not work correctly. Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
Die Herausforderungen bei der Nutzung von ChatGPT
This reference implementation, however, uses a stateless mode. The model was trained to use a python tool to perform calculations and other actions as part of its chain-of-thought. To improve performance the tool caches requests so that the model can revisit a different part of a page without having to reload the page. The model has also been trained to then use citations from this tool in its answers. To control the context window size this tool uses a scrollable window of text that the model can interact with.
LLM (Large Language Model)
Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. We are still considering release of the larger models. Code and models from the paper « Language Models are Unsupervised Multitask Learners ». We released the models with native quantization support. You can either use the with_python() method if your tool implements the full interface or modify the definition using with_tools(). To enable the python tool, you’ll have to place the definition into the system message of your harmony formatted prompt.
GPT (Generative Pre-trained Transformer)
Check out our awesome list for a broader collection of gpt-oss resources and inference partners. If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after installing Ollama. You can use vLLM to spin up an OpenAI-compatible web server. If you use Transformers’ chat template, it will automatically apply the harmony response format. You can use gpt-oss-120b and gpt-oss-20b with the Transformers library. Download gpt-oss-120b and gpt-oss-20b on Hugging Face
Zotero插件zotero-gpt
- The model was trained to use a python tool to perform calculations and other actions as part of its chain-of-thought.
- The code snippet will be executed, and the text returned by the code snippet will replace the code snippet.
- Download gpt-oss-120b and gpt-oss-20b on Hugging Face
- These implementations are largely reference implementations for educational purposes and are not expected to be run in production.
The following command will automatically download the model and start the server. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions – something which current NLP systems still largely struggle to do. 该API Key用于转发API，需要将Host改为api.chatanywhere.tech(国内首选)或者api.chatanywhere.org(国外使用)。
ChatGPT in der Bildung
This will work with any chat completions-API compatible server listening on port 11434, like ollama. While vLLM uses the Hugging Face converted checkpoint under gpt-oss-120b/ and gpt-oss-20b/ root directory respectively. This implementation is not production-ready but is accurate to the PyTorch implementation. This version can be run on a single 80GB GPU for gpt-oss-120b.
You can either use the with_browser_tool() method if your tool implements the full interface or modify the definition using with_tools(). To enable the browser tool, you’ll have to place the definition into the system message of your harmony formatted prompt. It also exposes both the python and browser tool as optional tools that can be used. Along with the model, we are also releasing a new chat format library harmony to interact with the model.
gpt-2
The reference implementations in this repository are meant as a starting point and inspiration. As a result the PythonTool defines its own tool description to override the definition in openai-harmony. During the training the model used a stateful tool which makes running tools between CoT loops easier. The torch and triton implementations require original checkpoint under gpt-oss-120b/original/ and gpt-oss-20b/original/ respectively. The terminal chat application is a basic example of how to use the harmony format together with the PyTorch, Triton, and vLLM implementations.
The code snippet will be executed, and the text returned by the code snippet will replace the code snippet. Undoubtedly, if you are familiar with Zotero APIs, you can develop your own code. For basic information, see our model card. We also recommend using BF16 as the activation precision for the model. This implementation runs in a permissive Docker container which could be problematic in cases like prompt injections. This implementation is purely for educational purposes and should not be used in production.
Additionally we are providing a reference implementation for Metal to run on Apple Silicon. We also include an optimized reference implementation that uses an optimized triton MoE kernel that supports MXFP4. In this implementation, we upcast all weights to BF16 and run the model in BF16.
- This implementation is not production-ready but is accurate to the PyTorch implementation.
- We include an inefficient reference PyTorch implementation in gpt_oss/torch/model.py.
- You can either use the with_python() method if your tool implements the full interface or modify the definition using with_tools().
- The model has also been trained to then use citations from this tool in its answers.
We include an inefficient reference PyTorch implementation in gpt_oss/torch/model.py. Gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. We may release code for evaluating the models on various benchmarks.
To run this implementation, pin up online casino the nightly version of triton and torch will be installed. It also has some optimization on the attention code to reduce the memory cost. If you want to try any of the code you can install it directly from PyPI