dsh-plugin-c2c
面向 DeepSeek Harness 的 Cache-to-Cache (C2C)：通过本地守护进程将 rosetta KV-cache fusion runtime (thu-nics/C2C) 提供为 c2c_* 工具
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:Alyosha28/dsh-plugin-c2c#2bdff64f436fcb7a66a4089f98b260cb2522b911Plugin C2c 以 dsh-plugin-c2c 发布，当前版本为 0.1.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
Cache-to-Cache for DeepSeek Harness. Two local LLMs that share a KV-cache instead of exchanging text.
A DSH plugin that exposes thu-nics/C2C
(Cache-to-Cache: Direct Semantic Communication Between Large Language Models,
ICLR'26) as six c2c_* agent tools.
C2C runs two models at once — a receiver that answers and one or more sharers — and projects the sharer's KV-cache into the receiver's cache space, fusing it before the receiver generates anything. The sharer contributes its internal state directly. No text passes between them.
When one model hands work to another today, the handoff is text: model A writes a completion, model B reads it as tokens. That channel is lossy by construction — whatever the tokens cannot spell, the receiver never learns.
The C2C paper's observation is that the KV-cache is the model's working state, so the handoff does not have to go through language at all. Project the cache into the receiver's space, fuse, and the receiver starts from a richer position. The paper reports 8.5–10.5% higher accuracy than either model alone and 3.0–5.0% over text-based communication.
This plugin does not invent any of that; it packages the upstream runtime so a DSH session can drive it. See Accuracy claims for what is and is not verified here.
# 1. set up the C2C runtime (clones upstream, builds a venv, pins transformers)
python python/dsh_c2c_setup.py --root ~/factor_digging/c2c --verify
# 2. install the plugin
dsh plugin --profile web add Alyosha28/dsh-plugin-c2c
The setup step is one command and does not download weights. The ~3.2 GB of model
files arrive on the first c2c_load, cached under <root>/models/hf.
Then restart the session — profiles on patchReload: startup load plugins at boot.
c2c_load pair: qwen3_0.6b+qwen2.5_0.5b # build the fused model
c2c_chat prompt: "..." # answer through the fusion
c2c_compare prompt: "..." # fused vs receiver-alone, side by side
c2c_unload # release device memory
c2c_compare is the interesting one: it runs the same prompt twice, once with the
sharer fused in and once with the receiver alone, and shows both answers. The two
conditions differ only in whether the projection is applied, so the difference you
see is attributable to the fusion.
Six tools total: c2c_status, c2c_models, c2c_load, c2c_chat, c2c_compare,
c2c_unload.
Seven fuser pairs ship upstream, from 0.6B+0.5B up to 8B+7B. The smallest runs comfortably on a laptop.
idleUnloadSeconds.arm64 Mac, 48 GB unified memory, macOS 26, torch 2.14.0, transformers 4.52.4,
float16 on MPS, smallest pair (Qwen3-0.6B receiver + Qwen2.5-0.5B-Instruct sharer):
| Load, weights on disk | 6.9–7.7 s (2 models + 28 projectors) |
| Decode, receiver only | 39 tok/s |
| Decode, C2C fused | 64 tok/s |
| Prefill + 96 tokens, fused | 1.5 s |
| Weights | ~3.2 GB |
DSH agent ── c2c_* tools ──HTTP/loopback──► c2c_daemon.py ──► rosetta (PyTorch)
(Node) (this plugin) (Python) receiver + sharer
+ C2C projectors
transformers is pinned to 4.52.4 by the setup script — this is an upper
bound, not a floor. rosetta clones KV caches by appending to
DynamicCache.key_cache, which is a plain list only through 4.54.x;
transformers 4.55 wraps it without .append() and 4.56 removes the attribute.
Both break the entire fusion path, and the failure only appears at model-load
time. Installing current transformers looks fine right up until it is not.accelerate is required: every upstream loader passes device_map=, which
transformers rejects without it. Upstream lists it only under an optional extra.Defaults work with no editing — repoRoot and pythonPath are auto-detected.
Override in the profile's cordis.patch.yml:
- id: c2c
config:
repoRoot: /path/to/c2c # default: auto-detect
pythonPath: /path/to/python # default: <repoRoot>/.venv/bin/python
port: 8765 # loopback daemon port
idleUnloadSeconds: 900 # 0 disables idle release
loadTimeoutSeconds: 600
@deepseek-ai/* packages are declared as peerDependencies, matching the other
published DSH plugins, and the plugin ships no node_modules. At runtime they
resolve through the installation's shared closure at $DSH_HOME/profiles/node_modules.
A symlinked development checkout is the exception: dsh plugin add <path>
installs a symlink, and Node resolves it to its real path, so the module walk starts
in your checkout and never reaches the shared closure. The bundled .npmrc sets
auto-install-peers=true so a plain pnpm install in the checkout materializes them.
The daemon is a standalone HTTP service and is usable without DSH:
python python/c2c_daemon.py --repo-root /path/to/c2c --port 8765
curl -s localhost:8765/models
curl -s -X POST localhost:8765/generate \
-H 'content-type: application/json' \
-d '{"prompt":"Say hello.","mode":"c2c","max_new_tokens":48}'
torch.multinomial is known to return
zero-probability indices there
(pytorch#192577), which
silently corrupts sampled output. Greedy decoding is unaffected and is the
default. The tool description records this.float16 on MPS, bfloat16 on CUDA, float32 on CPU. The
published projectors are stored bfloat16; they are re-cast to the receiver's dtype
at construction.PYTORCH_ENABLE_MPS_FALLBACK=1 is set automatically, so an op with no MPS kernel
falls back to CPU rather than raising mid-generation.The paper reports 8.5–10.5% accuracy gains over individual models. This plugin has
not measured that. What is verified here is that the pipeline runs end to end and
that the fusion changes the output — a smoke test, not a benchmark. Treating a
single c2c_compare result as evidence of the paper's numbers would be wrong; that
needs a benchmark subset evaluated per condition.
kv_cache_index); this plugin does not expose it yet.python/verify_c2c.py is a manual end-to-end check.The fusion method and the rosetta runtime are
thu-nics/C2C by Tianyu Fu, Zihan Min, Hanling
Zhang, Jichao Yan, Guohao Dai, Wanli Ouyang and Yu Wang. If you use the method,
cite their paper:
@article{fu2025c2c,
title={Cache-to-Cache: Direct Semantic Communication Between Large Language Models},
author={Tianyu Fu and Zihan Min and Hanling Zhang and Jichao Yan and Guohao Dai and Wanli Ouyang and Yu Wang},
journal={arXiv preprint arXiv:2510.03215},
year={2025},
}
This repository packages that runtime for DSH. It contains no model weights and vendors none of their code.
MIT. Upstream C2C is MIT as well.