gpt-oss-140b-ren-2

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.