Samosa-Chat-Qwen3.6-35B-A3B-group32

<div align="center"> <img src="samosa-chat.png" alt="Samosa Chat mascot" width="190"> <h1>Samosa Chat</h1> <p><strong>Run Qwen3.6-35B-A3B locally on a 16 GB machine.</strong></p> <p>In your terminal, or in your browser · Runs on the CPU · No cloud account · No telemetry</p> </div>

Foundation and model credit. Samosa Chat is built on colibrì by JustVugg; its expert-streaming design, SIMD kernels, and utility headers made this project possible. The converted checkpoint comes from Qwen/Qwen3.6-35B-A3B by the Qwen team. This is an independent, unofficial Apache-2.0 text-only conversion. It is not affiliated with or endorsed by either upstream team.

What Samosa adds: its own Qwen3.6 engine in C, the group-32 format and its converter, the expert cache that fits 35B parameters into 16 GB, sealed resumable conversations, a local server and app, and an atomic installer. Details.

This repository hosts the model that powers Samosa Chat. These are the group-32 model files used by Samosa Chat, a free, open-source project that runs Qwen3.6-35B-A3B fully locally on a 16 GB machine. The complete source, documentation, and issue tracker live on GitHub: github.com/deepanwadhwa/samosa-chat

Samosa Chat packages the group-32 int4 model and a dependency-free C inference engine. You use it from the terminal (samosa "your question"), which is the normal way, or through a local browser app (samosa app), which is currently a demo. Everything stays on your computer: no account, no telemetry, and no remote request during inference. The server binds only to 127.0.0.1.

What Samosa adds

Qwen's checkpoint and the colibrì runtime are the starting point. Samosa adds:

• A Qwen3.6 engine in C, covering the 30 Gated DeltaNet layers, the 10 gated attention layers, the shared/routed MoE path, the tokenizer, and the chat template.
• The group-32 format and its converter — a shard-by-shard conversion of the original checkpoint into a manifest-based expert container. Group-32 gives every 32 weights their own int4 scale instead of one scale per whole matrix row, which is what makes the output quality hold up.
• A byte-budgeted expert cache with LRU eviction, per-layer floors, reusable slabs, memory-pressure monitoring, and I/O telemetry — the piece that lets a 35B model run in 16 GB by streaming experts from SSD.
• Sealed, resumable conversations (QWSESS01): geometry-checked, SHA-256-sealed, atomically written, and byte-exact on resume.
• A dependency-free local server and browser app in C: JSON/SSE, a bounded queue, cancellation, health telemetry, and clean shutdown.
• An atomic installer that verifies every byte, compiles and smoke-tests an inactive release, and switches only on success — rolling back otherwise.
• The tests around all of it: quantized kernels, KV math, sessions, server behaviour, stop conditions, the wrapper, installer rollback, and PATH setup.

Full source and detail: github.com/deepanwadhwa/samosa-chat

Supported hardware

macOS on Apple Silicon (arm64) runs natively via the one-line installer, with at least 16 GB of RAM. Linux and Windows run via Docker, from the repository. Every measurement below is from the machine named beside it:

macOS is the fast path; x86 is ~4–5x slower, for a known and fixable reason. The engine picks its vectorised kernels at compile time and the build passes no -march, so on x86 the AVX2 kernels are compiled out and it falls back to a scalar loop — measured 7.6x slower on identical hardware. The Zenbook above has AVX2 and gets none of it. The fix is runtime CPU dispatch; it is tracked in the repository as G10/H2.

Output is identical across platforms: the same prompt and seed returns the same tokens on macOS/NEON, arm64 Linux, and x86_64 Linux, at the same ~3.84 GB footprint. Only speed differs.

“CPU-only” describes the inference backend: NEON/SDOT on Apple Silicon, the scalar or AVX2 path on x86, optional OpenMP — not Metal, and not CUDA. It does not mean an arbitrary 16 GB laptop runs this well. It needs an NVMe SSD (expert weights stream from disk on every token; a host bind mount instead of a named Docker volume costs ~6x, and a hard drive is unusable) and ≥6 GB given to the Docker VM on Linux/Windows. M1/M2 and other machines still need independent release validation.

Install

macOS (Apple Silicon)

curl -fsSL https://huggingface.co/deepanwa/Samosa-Chat-Qwen3.6-35B-A3B-group32/resolve/main/install.sh | sh

Then open a new terminal and ask it something:

samosa "explain how DNS works"

Allow roughly 30 GB free. The installer downloads into an inactive versioned release, verifies every byte size and SHA-256 digest, compiles the C engine, and smoke-tests it before atomically switching the live release. A corrupt or interrupted upgrade leaves the prior release untouched. No administrator rights are required.

Linux and Windows

Use Docker, from the GitHub repository — the installer above is macOS-only:

git clone https://github.com/deepanwadhwa/samosa-chat
cd samosa-chat
docker build -t samosa .
docker volume create samosa-model
docker run --rm -v samosa-model:/model samosa pull
docker run -d --name samosa -p 127.0.0.1:8642:8642 -v samosa-model:/model --memory=6g samosa serve

Then open http://127.0.0.1:8642. samosa pull fetches these model files into the volume and verifies each one by size and SHA-256; an interrupted download resumes if you re-run it. On Windows this runs inside WSL2 — the full step-by-step, including the Docker VM memory requirement, is in the repository README.

Expect ~1.3 tok/s on x86 today rather than the Mac's 5–7 — see Supported hardware for why.

Everything installs under ~/.samosa — the samosa command at ~/.samosa/bin/samosa, the active release at ~/.samosa/current, and your conversations at ~/.samosa/chats. Nothing is installed system-wide. The installer adds ~/.samosa/bin to PATH via one line in your shell rc file, which only affects terminals opened afterwards — hence the new terminal above. If samosa is still not found, either export PATH="$HOME/.samosa/bin:$PATH" in the current shell, or run it directly as ~/.samosa/bin/samosa "how are you". To uninstall, delete ~/.samosa and remove that line.

Two ways to use it

The terminal is the normal way to use Samosa.

samosa "explain how a hash table handles collisions"
samosa --continue "and which strategy does Python use?"
samosa --think "solve this logic puzzle"
samosa --think-code "build a responsive settings page"
samosa --fast "summarize this design"
samosa --max-tokens 2048 "write a long explanation"
samosa doctor

The browser app is a demo at this point. It works, and it is the nicest way to watch answers stream and see the model's reasoning, but it exists to show the engine off rather than as a polished interface. samosa app starts one resident local model process and opens it; samosa serve --stop stops it. It provides:

• token-by-token answer streaming;
• a separate collapsible thinking view;
• direct, general-thinking, and precise-code profiles;
• stop/cancel, seed, and output-ceiling controls;
• browser-local conversation display history plus sealed server snapshots;
• tokens/s, RSS, and thinking-closure telemetry;
• responsive light/dark presentation with no framework, CDN, analytics, or external script.

The complete UI and logo total 181,552 bytes in this release.

Local API

The resident server exposes local JSON/SSE chat completions at POST /v1/chat/completions, plus health, model discovery, cancellation, and clean shutdown endpoints. Requests are serialized through a bounded queue; the model is never mutated by concurrent generations.

Generation stops when Qwen emits its end-of-turn token. The outer ceiling is 8,192 new tokens. General reasoning defaults to a 1,024-token internal budget and precise code to 2,048. On budget exhaustion Samosa appends Qwen's trained early-stop transition before </think> instead of injecting a bare control token. Natural closure, budget transition, repetition stop, cancellation, and length stop remain distinct in telemetry.

Saved history, the newly tokenized turn, and the requested completion ceiling must fit a 24,576-token total context cap. Samosa rejects an oversized request before queueing or allocating KV state. One conversation state is resident at a time; other conversations remain as sealed snapshots on disk.

Stopping a generation is safe for the saved conversation: a cancelled turn is persisted only up to its last complete sentence, so an interrupted answer does not poison later turns. If it produced no complete sentence, the previous snapshot is kept and session_saved reports false.

Model layout

Qwen describes the upstream language model as 35B parameters total with 3B activated per token, 40 layers, 256 routed experts, and 8 routed plus 1 shared expert active per MoE layer. Samosa keeps dense weights resident and streams only selected routed experts from SSD.

The previous whole-row q4 artifact had materially higher measured weight reconstruction error. This release uses group size 32. The format is custom to Samosa and is not a GGUF or Transformers-loadable checkpoint.

Measured behavior

All measurements below are from the 16 GB M3 MacBook Air reference machine. They are workload-specific observations, not guarantees for every Mac.

A bounded release-path check served the real UI and logo, streamed exactly Samosa app works locally., stopped naturally on Qwen's end-of-turn token, saved the conversation snapshot, decoded at 5.13 tok/s, and peaked at 3.28 GB RSS. The full bounded test suite covers grouped quantization, sessions, server behavior, special-token stopping, cancellation, the wrapper, and corrupt atomic-upgrade rollback.

For the resident app, macOS physical footprint measured 2.51 GiB after model load and 4.07 GiB after a real two-turn continuation; the independent footprint tool reported 2,566 MiB and 4,170 MiB. A live Activity Monitor comparison also matched the value displayed by Samosa. GQA KV grows by about 40 KiB per context token, so the 24,576-token cap bounds that variable component to about 960 MiB. The allocator may retain its high-water mark, but chat history cannot grow KV memory beyond the cap.

Long thinking is expensive: one 933-token group-32 control reread 376.77 GB of expert data. More output tokens can be useful, but they increase time, power draw, and heat even when RAM stays bounded. SSD reads do not consume drive endurance (TBW is write-only); the genuine cost is speed and energy.

Privacy and storage

• Normal inference does not contact Hugging Face or any other remote service.
• The installer contacts Hugging Face only to download public release files.
• The listener is hard-bound to IPv4 loopback.
• Model files are read-only during inference.
• A continuing conversation atomically replaces a roughly 63–70 MB sealed session snapshot at turn end.
• Display transcripts are currently stored in the browser's local storage.

Known limitations

• Only Apple Silicon macOS is supported; only one M3 16 GB machine has completed the full test program so far.
• Metal acceleration is not implemented. The CPU path is partly constrained by repeated SSD expert reads; a future Metal backend must be measured end-to-end rather than assumed faster.
• Group-32 improves the original quantization error, but broad benchmark and upstream-parity coverage remains incomplete.
• Deleting a conversation in the UI removes its browser transcript but does not yet remove the sealed server snapshot.
• In-RAM conversation slots, document chat, and web access remain roadmap work.
• Text only: the Qwen vision tower, image/audio/video input, and tool calling are not included.
• SSD speed is the primary performance bottleneck. Routed experts are streamed from storage on every token; NVMe bandwidth directly determines tok/s. Measured: 2.3+ GB/s (native NVMe) gives 5-7 tok/s; ~0.5 GB/s (a Docker host bind mount through virtiofs) gives ~0.9 tok/s.
• SSD reads do not consume drive endurance. Flash endurance is rated in TBW (Terabytes Written, JEDEC JESD218) and DWPD (Drive Writes Per Day); program/ erase cycles wear NAND, reads do not. Read disturb is real but its thresholds are orders of magnitude beyond this workload. Stated from the endurance rating definition, not from a SMART measurement on the reference machine — Apple Silicon's internal NVMe does not expose endurance counters. The genuine costs of streaming are speed, power, and heat.

Source and detailed evidence: github.com/deepanwadhwa/samosa-chat

License

Apache-2.0. The source repository includes LICENSE and NOTICE with the complete derivative-work attribution.