Note: This is an improvement over the previous impotent heresy version, increasing the willingness of the model further. Tinkering with the generation parameters can improve output in particular usecases. This one also preserves the MXFP4 quantisation in the base model. MagicalAlchemist pointed out that a jailbreak prompt perfects the model's uncensored output. Let me know how you find it.
Heretication Results
Score Metric
Value
Parameter
Value
Refusals
15/100
direction_index
per layer
KL Divergence
0.2798
attn.o_proj.max_weight
1.31
Initial Refusals
98/100
attn.o_proj.max_weight_position
14.39
attn.o_proj.min_weight
1.25
attn.o_proj.min_weight_distance
1.10
mlp.down_proj.max_weight
1.40
mlp.down_proj.max_weight_position
16.46
mlp.down_proj.min_weight
0.29
mlp.down_proj.min_weight_distance
2.55
Degree of Heretication
The Heresy Index weighs the resulting model's corruption by the process (KL Divergence) and its abolition of doctrine (Refusals) for a final verdict in classification.
Index Entry
Classification
Analysis
Absolute
Absolute Heresy
Less than 10/100 Refusals and 0.10 KL Divergence
Tainted
Tainted Heresy
Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence
Impotent
Impotent Heresy
Anything above 25/100 Refusals and 0.21 KL Divergence
Note: This is an arbitrary classification inspired by Warhammer 40K, having no tangible indication towards the model's performance.
Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
[!NOTE]
This model card is dedicated to the smaller gpt-oss-20b model. Check out gpt-oss-120b for the larger model.
Highlights
Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
Agentic capabilities: Use the models’ native capabilities for function calling, web browsing, Python code execution, and Structured Outputs.
MXFP4 quantization: The models were post-trained with MXFP4 quantization of the MoE weights, making gpt-oss-120b run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the gpt-oss-20b model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
Inference examples
Transformers
You can use gpt-oss-120b and gpt-oss-20b with Transformers. If you use the Transformers chat template, it will automatically apply the harmony response format. If you use model.generate directly, you need to apply the harmony format manually using the chat template or use our openai-harmony package.
To get started, install the necessary dependencies to setup your environment:
pip install -U transformers kernels torch
Once, setup you can proceed to run the model by running the snippet below:
vLLM recommends using uv for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
