---
license: other
library_name: transformers
base_model: openai/gpt-oss-120b
pipeline_tag: text-generation
tags:
- gpt-oss
- code
- agentic
- research-preview
- custom-code
- vllm
- mxfp4
---

# gpt-oss-140b Ren-2

`gpt-oss-140b`, codename `Ren-2`, is an agentic / SWE-oriented derivative of OpenAI GPT-OSS 120B.

This release takes the 120B base model and adds roughly `20B` more parameters oriented toward agentic coding and SWE-style behavior.

## Overview

- Base model: `openai/gpt-oss-120b`
- Release name: `gpt-oss-140b Ren-2`
- Format: `MXFP4`
- Intended use: coding, agentic coding, SWE-style assistant workflows
- Status: research preview

Ren-2 is meant to feel like a more agentic version of GPT-OSS 120B rather than a generic continuation of the base checkpoint.

## Training

- Built on a custom framework
- Roughly `3` hours of pre-training / post-training work for this release path
- Expanded from the 120B base with roughly `20B` additional parameters

This is an iterative open release. More sizes and follow-up revisions will come later.

## Inference

This model was tested with:

- `vLLM 0.19.0`

Recommended serving settings:

- `num_experts_per_tok=12`
- `--reasoning-parser openai_gptoss`
- `--tool-call-parser openai`
- `--enable-auto-tool-choice`

The original base setup used `4` active experts per token. Ren-2 is intended to run at `12` active experts per token.

Rough active-equivalent compute:

- original `top-k=4`: about `5.7B` active-equivalent params
- Ren-2 `top-k=12`: about `12.9B` active-equivalent params

These are approximate active-equivalent numbers, not total parameter counts.

In internal agentic-task traces at `top-k=12`, roughly half of active routing traffic ran through the added `20B` expansion. Observed new-expert usage in those traces was about `48.6%` of active expert selections and about `46.2%` of routing mass. This is workload-dependent.

This release is intended to run directly from the baked model shards. No extra router merge step is required at inference time.

## What It Is Good At

- coding
- agentic coding
- SWE-style assistant behavior
- practical tool-using workflows

Ren-2 is intended to be usable for production-style coding and agentic workflows, including terminal coding agents, SWE assistants, and tool-using automation setups.

## Feedback

Useful feedback includes:

- coding quality
- tool use quality
- long-context behavior
- inference stability
- preferred smaller sizes / VRAM targets

If you want smaller custom models, reach out with the hardware target and the kind of feedback you can provide.

It can be a different size or architecture, as long as the feedback loop is useful.

## Included Files

- `config.json`
- `generation_config.json`
- `tokenizer.json`
- `tokenizer_config.json`
- `chat_template.jinja`
- `model.safetensors.index.json`
- `model-*.safetensors`
- `README.md`

## License

Replace the placeholder `license: other` metadata with the actual license you want to publish under after confirming compatibility with the base model and your added weights.