---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
new_version: EpistemeAI/metatune-gpt20b-R1.1
---
## Model Card
### We release open-weight metatune-gpt20b, fine tuned version of OpenAI's gpt-oss-20b model,  this is one of the first public release recursive self improving AI.
- Generates new data for itself,
- Evaluates its performance, and
- Adjusts its own hyperparameters based on improvement metrics.

## Use cases: 
- genuinely demonstrate scientific and mathematical understanding at a postdoctoral level.
- coding
- - Topics: Euler–Lagrange equation, vector calculus, statistical mechanics
 

## Guardrails:
- generally, please set reasoning = "high", it will usually prevent jailbreaking and prompt injection
- use safety gpt oss 20b for guardrails before this model:  [openai/gpt-oss-safeguard-20b](https://huggingface.co/openai/gpt-oss-safeguard-20b)

# 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](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.

To get started, install the necessary dependencies to setup your environment:

```
pip install -U transformers kernels torch 
```

For Google Colab (free/Pro)
```
!pip install -q --upgrade torch

!pip install -q transformers triton==3.4 kernels

!pip uninstall -q torchvision torchaudio -y
```

Once, setup you can proceed to run the model by running the snippet below:

```py
from transformers import pipeline
import torch
model_id = "EpistemeAI/metatune-gpt20b-R1"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)
messages = [
    {"role": "user", "content": "Derive the Euler–Lagrange equation from the principle of stationary action.""},
]
outputs = pipe(
    messages,
    max_new_tokens=3000,
)
print(outputs[0]["generated_text"][-1])
```
# Reasoning levels

You can adjust the reasoning level that suits your task across three levels:

* **Low:** Fast responses for general dialogue.  
* **Medium:** Balanced speed and detail.  
* **High:** Deep and detailed analysis.

The reasoning level can be set in the system prompts, e.g., "Reasoning: high".

# Tool use

The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks

# Fine-tuning

Both gpt-oss models can be fine-tuned for a variety of specialized use cases.

This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.

# Benchmark

|             Tasks      |metatune R0|metatune R1|Llama 4 Maverick|
|:-----------------------|:-----|:-----|:----- |
|gsm8k_cot               |0.91  |0.9796|   -   |
|gpqa_diamond_cot_n_shot |0.722 |      |  -    |
|hellaswag               |0.421 |**0.525**|-      |
|arc_challenge           |0.349 | 0.349|-      |
|winogrande              |**0.7851**| 0.5928|-     |

# Inspiration:
Jurgen Schmidhuber



# Uploaded finetuned  model

- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit

This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)