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---
name: architecture-reference
description: "Explains RWKV-7 tensor shapes, checkpoint export, context-parallel
state composition, and RWKV-7/Qwen comparison workflows."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
---
# RWKV architecture reference
Use this route when the request is about tensor names, state layout, checkpoint
conversion, RWKV-7 versus Qwen3.5 comparisons, or why two implementations do
not match numerically.
## Route by task
- **Understand model shape or state layout**: read
[architecture-details.md](references/architecture-details.md).
- **Export a Qwen text checkpoint**: run
[export_qwen_text_state.py](scripts/export_qwen_text_state.py) or read its
help text first.
- **Reason about chunk/state equivalence**: read
[context-parallelism.md](references/context-parallelism.md).
- **Debug a mismatch**: read [troubleshooting.md](references/troubleshooting.md)
before changing the tokenizer, tensor naming, or checkpoint path.
## What this route owns
- RWKV-7 block names, layer/state shapes, and the role of `emb.weight`, `ln0`,
`ln_out`, and the per-block attention/FFN tensors.
- Qwen3.5 text-only export and why vision/MTP tensors are excluded from that
export path.
- The chunk-merging identity used by RWKV-7 context parallelism.
- Compatibility notes for the legacy v5/v6 code paths when they help explain a
shape or naming difference.
## What this route does not own
- Data conversion and `magic_prime` selection belong to `training-data`.
- Prompt/sampling and MMLU evaluation belong to `inference-evaluation`.
- ROSA toy experiments belong to `rosa-experiments`.
## Bundled helper
- `scripts/export_qwen_text_state.py` accepts a model id or local Hugging Face
directory and writes a text-only checkpoint plus metadata file. It is the
safe replacement for the repository's checkpoint-export example because it
requires explicit output paths and has no hard-coded local checkpoint.
## Handoff
Use this route when you need shapes, tensor names, or conversion/composition
facts, not when you need to actually train or sample from the model.
