Setup guide · version 2026-10-01

Signal 3.8 · AP-Q6_K

A reproducible candidate and cautious setup path. This guide does not establish that it will succeed on every device.

Before you download

Inspect existing hardware, free RAM/VRAM, disk, models and runtime. Confirm the selected hardware and memory; never assume separate GPUs form one memory pool.
Check the model license, exact pinned GGUF filename, revision and checksum below. Check the report's historical-byte verification notes. Keep existing models and configs.
Use a current compatible llama.cpp build, or LM Studio with an engine supporting this architecture and template. Follow its official model-download guide; do not silently substitute another quant or latest revision.
Begin with a modest context and one session. Inspect real allocation before raising limits. Weights, cache, buffers and other apps all need memory. Recorded long-context/MTP settings are not guaranteed on other versions.
Keep any local endpoint bound to loopback. Consult your local agent’s provider documentation. Pi/Hermes need compatible provider and tool parsing; chat assistants may only be able to explain manual steps.
Run a bounded scratch task with measurable acceptance checks. Record quant/hash/runtime/template/context, elapsed time, memory, corrections and failures. Stop on memory pressure or repeated malformed tool calls. Installation and configuration changes follow your agent’s own approvals.

Settings, artifact identity & publisher claim

Recorded: temperature .6, top-p .95, top-k 20, min-p .05, presence penalty 1; q8_0 K/V; native MTP max 4 and backend draft sampling off. Recorded 118,016 context is not a safe default for every device.

Settings source · apache-2.0

AP-Q6_K/Signal-3.8-27B-AP-Q6_K.gguf
Revision: ae272aa3e533854feebcfa1ded0f1dca9afedcef
SHA-256: fe4760215d62b697686a7e608a9705b629003103228228b37e104da22e2385c0

Pinned download metadata checked October 1. Historical reports do not verify their bytes against this SHA-256; matching names/quantizations are not proof of identical files.

Publisher/author claim: Author emphasizes faster useful answers and provides sampler/cache guidance. No numerical reduction claim is entered here. Source

Claim is attributed, not independently confirmed; it does not determine ordering. No comparable vendor/community thinking-reduction percentage is available.

27B total parameters · dense. Full stored weights determine memory.

AI Efficiency Toolbox evidence review · 2026-10-01 · Qualified lead. Jason’s strongest observed local coding result in the published comparison. One recorded run, no independent community coverage in this selection; hardware applicability unverified.

Initial curated review, October 1, 2026. No representative survey or new benchmark.

Copy an agent setup handoff

Includes your selected hardware/task and the exact artifact. Your agent must check resources and its own permissions; without computer tools it can guide manual setup.

Help me evaluate this local AI setup. Guide version: 2026-10-01.
Guide: https://ai.efficiencytoolbox.com/local-llm-finder/setups/signal-q6
Hardware: Unknown — ask me before downloading or changing anything; selected unified/system memory: unknown.
Execution mode: local-only. This guide covers a local candidate only; cloud/hybrid comparisons are unevaluated.
Work: Build or fix code. Current setup: not specified.
Exact candidate: Signal 3.8 · AP-Q6_K, AP-Q6_K GGUF.
Trusted artifact publisher: agentionai/Signal-3.8-27B-GGUF. Revision: ae272aa3e533854feebcfa1ded0f1dca9afedcef.
Download: https://huggingface.co/agentionai/Signal-3.8-27B-GGUF/resolve/ae272aa3e533854feebcfa1ded0f1dca9afedcef/AP-Q6_K/Signal-3.8-27B-AP-Q6_K.gguf
SHA-256: fe4760215d62b697686a7e608a9705b629003103228228b37e104da22e2385c0. Weight file: 22431000128 bytes. License: apache-2.0.
Recorded runtime: llama.cpp 2.37.0. LM Studio is an optional GGUF path only if its selected engine supports this architecture/template; it does not reproduce every standalone runtime feature.

1. Inspect my OS, hardware, free RAM/VRAM, disk space, existing models, runtime and agent configuration first. Confirm unknown values and any mismatch with this guide. Do not silently pool separate GPU memories.
2. Honor your own tool/installation/network approvals. Do not replace or delete existing models/configs, expose credentials, open the server publicly, or spend money without my approval. If you lack computer tools, explain the manual steps instead of claiming installation.
3. Review model license and source. Download only the exact pinned artifact after confirming resources. Verify SHA-256 before loading. Total weights plus KV cache, context, buffers/projector and other apps must fit; active MoE parameters are not weight memory. Start with a modest context and one session, inspect actual allocation; this guide supplies no guaranteed context fit.
4. Use a compatible llama.cpp build, or LM Studio from https://lmstudio.ai/ with its official guide https://lmstudio.ai/docs/app/basics/download-model . Search the exact repository/filename; if its UI offers different bytes/revision, use the pinned download/import workflow documented by the installed version rather than substituting silently. Check architecture/template/tool-call support. Keep local endpoints bound to loopback.
5. Settings and limitations: Recorded: temperature .6, top-p .95, top-k 20, min-p .05, presence penalty 1; q8_0 K/V; native MTP max 4 and backend draft sampling off. Recorded 118,016 context is not a safe default for every device.
Settings source: https://ai.efficiencytoolbox.com/downloads/14-local-ai-setups/signal-settings.txt
6. With Pi/Hermes or another tool-capable local-model harness, verify its model/provider configuration against its current documentation. Codex/Claude/ChatGPT/Gemini may guide setup but are not all local-model runtimes or computer-capable agents. Do not invent a provider URL or assume support. Run a bounded scratch task, validate tool arguments, outputs and tests, record runtime/version/quant/context and memory/time. Stop if memory pressure, loops or malformed tools recur.
7. Explain what worked, failed or needed correction. Keep publisher claims separate from community reports and this test. Pinned download metadata checked October 1. Historical reports do not verify their bytes against this SHA-256; matching names/quantizations are not proof of identical files.
Editorial limitation: Jason’s strongest observed local coding result in the published comparison. One recorded run, no independent community coverage in this selection; hardware applicability unverified.

Evidence and limits

Jason’s strongest observed local coding result in the published comparison. One recorded run, no independent community coverage in this selection; hardware applicability unverified.

Pinned download metadata checked October 1. Historical reports do not verify their bytes against this SHA-256; matching names/quantizations are not proof of identical files.

Jason’s published Signal observation · checked 2026-10-01

Publisher claims and community reports are attributed evidence, not independent verification of your installation. No automatic install or device access is performed by this website.