Borrowing it
Nothing to install: this file belongs to architehc/nanochat-rs-ternary. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/architehc/nanochat-rs-ternary/main/CLAUDE.mdgit clone --depth 1 https://github.com/architehc/nanochat-rs-ternaryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/architehc/nanochat-rs-ternary/claude-md)<a href="https://agentmods.dev/instructions/architehc/nanochat-rs-ternary/claude-md"><img src="https://agentmods.dev/badge/instructions/architehc/nanochat-rs-ternary/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/architehc/nanochat-rs-ternary/claude-md"><img src="https://agentmods.dev/badge/instructions/architehc/nanochat-rs-ternary/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.09883 | $0.09883 |
| Opus 5 | $0.04942 | $0.04942 |
| Sonnet 5 | $0.01977 | $0.01977 |
| Haiku 4.5 | $0.00988 | $0.00988 |
Grade A, and why
nanochat-rs-ternary CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 995 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — nanochat-rs Ternary + mHC-lite Implementation Plan
For Claude Code. Execute phases in order. Each phase has a test gate — do not proceed to the next phase until the gate passes. All code lives in a Cargo workspace rooted at
nanochat-rs-ternary/. Hardware target: dual AMD EPYC 9654 (224T, 1TB DDR5) + NVIDIA RTX PRO 6000 Blackwell (96GB).
Repository Layout (create first)
nanochat-rs-ternary/
├── Cargo.toml # workspace root
├── CLAUDE.md # this file
├── crates/
│ ├── ternary-core/ # Phase 1: packing, planar SoA, GGUF
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── lib.rs
│ │ ├── encode.rs # BitNet encoding (11=-1)
│ │ ├── pack.rs # pack/unpack, group quantization
│ │ ├── planar.rs # PlanarWeights: SoA layout, aligned alloc
│ │ ├── gguf.rs # GGUF reader/writer for ternary types
│ │ └── verify.rs # Triangle of Truth (Rust side)
│ │
│ ├── ternary-kernels/ # Phase 2: CPU + GPU compute kernels
│ │ ├── Cargo.toml
│ │ ├── build.rs # cc::Build for C kernels, cuda compilation
│ │ ├── csrc/
│ │ │ ├── ternary_gemv.c # adapted from ternary_final.c (v3.3.1)
│ │ │ ├── ternary_gemv.h # C API header
│ │ │ └── ternary_dp4a.cu # GPU decode kernel
│ │ └── src/
│ │ ├── lib.rs
│ │ ├── cpu.rs # safe Rust wrappers over C FFI
│ │ ├── gpu.rs # CUDA kernel launch wrappers
│ │ └── dispatch.rs # runtime CPU feature detection + dispatch
│ │
│ ├── mhc-lite/ # Phase 3: mHC-lite residual connections
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── lib.rs
│ │ ├── n2.rs # MhcLiteN2 (1 param, identity ↔ swap)
│ │ ├── n4.rs # MhcLiteN4 (24 perms, full BvN)
│ │ ├── verify.rs # doubly stochastic checks, composite gain
│ │ └── io.rs # binary serialization (matches Python export)
│ │
│ ├── nanochat-model/ # Phase 4: transformer architecture
│ │ ├── Cargo.toml
│ │ └── src/
│ │ ├── lib.rs
│ │ ├── config.rs # model configs (d20, 7B, 25B-MoE, 80B-MoE)
│ │ ├── embed.rs # token + position embeddings
│ │ ├── norm.rs # RMSNorm
│ │ ├── attention.rs # MHA / GQA / MLA (with DeltaNet option)
│ │ ├── ffn.rs # SwiGLU FFN (with MoE option)
│ │ ├── bitlinear.rs # BitLinear: ternary GEMV dispatch
│ │ ├── block.rs # TransformerBlock with mHC wiring
│ │ └── model.rs # full model: embed → blocks → head
│ │
│ └── nanochat-serve/ # Phase 6: inference server
│ ├── Cargo.toml
│ └── src/
│ ├── main.rs # CLI + Axum HTTP server
│ ├── engine.rs # KV-cache, sampling, batched decode
│ └── api.rs # OpenAI-compatible /v1/chat/completions
│
├── training/ # Phase 5: PyTorch training
│ ├── mhc_lite.py # mHC-lite module (BvN, exact DS)
│ ├── ternary_qat.py # BitLinear STE, absmean quantization
│ ├── model.py # nanochat architecture in PyTorch
│ ├── train.py # training loop (Muon+Lion, WSD schedule)
│ ├── export.py # PyTorch → GGUF + mHC binary export
│ └── requirements.txt
│
├── tests/ # Integration tests
│ ├── triangle_of_truth.rs # cross-validate all kernel paths
│ ├── mhc_property_tests.rs # doubly stochastic invariants
│ ├── roundtrip_test.rs # pack → GGUF → load → GEMV → verify
│ └── e2e_generate.rs # full model forward pass sanity
│
└── benches/
├── gemv_bench.rs # criterion benchmarks for all kernel paths
└── mhc_overhead.rs # verify mHC adds <0.001% overhead
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 995 lines · 9,883 tokens per session scan A e41222aa8f9a
nanochat-rs-ternary CLAUDE.md is an instructions file published in the GitHub repository architehc/nanochat-rs-ternary (22 stars, last pushed 1mo ago), licensed MIT. It adds 9,883 tokens to every session, about $0.0494 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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