# RWKV7-G1j arrives as a 13.3B attention-free model

> The latest RWKV7 checkpoint scales the recurrent, attention-free architecture to 13.3 billion parameters under a permissive Apache 2.0 license.

Published by The Open Weights on Sep 2, 2026. Canonical: https://theopenweights.com/news/rwkv7-g1j-13-3b-qmte

## Key facts

- Company: Fla Hub
- Model: RWKV7-G1j-13.3B
- Version: G1j-13.3B
- Category: Text / LLM
- Modalities: Text / LLM
- License: Apache 2.0 (Open weights, commercial use allowed)
- Parameters: 13.3B
- Context window: 16K tokens
- Architecture: RWKV7ForCausalLM
- Precision: BF16
- Weights size: 26.5 GB
- Significance: notable
- Published: 2026-09-02
- Last verified: 2026-09-02
- Hugging Face: https://huggingface.co/fla-hub/RWKV7-G1j-13.3B-20260831
- Canonical URL: https://theopenweights.com/news/rwkv7-g1j-13-3b-qmte

The RWKV project has published RWKV7-G1j, a 13.3-billion-parameter language model that continues the family's bet on a recurrent, attention-free design. The checkpoint is available now on [Hugging Face](https://huggingface.co/fla-hub/RWKV7-G1j-13.3B-20260831) under the permissive Apache 2.0 license, making it freely usable for research and commercial work alike.

Unlike the transformer models that dominate the field, RWKV replaces self-attention with a recurrent mechanism. In practice that means the model processes tokens sequentially while carrying a fixed-size state, rather than attending over an ever-growing context window. The appeal is efficiency: memory and compute during inference stay roughly constant per token, which can make long-sequence generation cheaper than in comparable attention-based systems.

## Why it matters

Most of the open-weights ecosystem has converged on the transformer, so a serious 13B-scale release from an alternative lineage is worth noting. RWKV7-G1j lands in the same size class as many popular open models while offering a different set of trade-offs:

- Attention-free recurrent architecture with constant per-token inference cost
- Multilingual coverage rather than an English-only focus
- A dense 13.3B parameter design (not a mixture-of-experts)
- Apache 2.0 licensing with no usage gates

The release record lists no fixed context length, which is consistent with a recurrent model that is not bounded by a hard attention window. As always, teams should validate the model against their own workloads before deploying it, but its licensing and architecture make it a useful point of comparison for anyone weighing alternatives to transformer baselines. Full weights and details are on the [model page](https://huggingface.co/fla-hub/RWKV7-G1j-13.3B-20260831).

## Get the model

- [Hugging Face](https://huggingface.co/fla-hub/RWKV7-G1j-13.3B-20260831)

## Sources

- [fla-hub/RWKV7-G1j-13.3B-20260831](https://huggingface.co/fla-hub/RWKV7-G1j-13.3B-20260831) — Hugging Face, Sep 2, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "RWKV7-G1j arrives as a 13.3B attention-free model", The Open Weights, Sep 2, 2026, https://theopenweights.com/news/rwkv7-g1j-13-3b-qmte