BTL-3-Compact

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BTL-3 Compact

The complete 27B agent model in one 8.39 GB file

Under 2.5 effective bits per parameter · 98.4% held-out tool-contract retention

Full BTL-3 · Runtime source · Bad Theory Labs · Discord

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Canonical behavior refresh — July 24, 2026: the file at the existing model/BTL-3-Compact-AVQ2.gguf path now embeds the rank-8 step-160 Thinking Escape behavior adapter. The packed decoder and rank-32 output correction are unchanged. The new model SHA-256 is 0a4d9ddee49e5aa93586a792bd4d452ea837229d49d22e54212dde87a5c9888a. Thinking remains disabled by default; thinking-enabled coding still fails its release gate and should be treated as experimental.

Introducing BTL-3 Compact

BTL-3 is a 27B open-weight agent model built for agentic coding, structural tool use, repository work, failure recovery, and long multi-turn execution. BTL-3 Compact packages the complete text model into one 8.39 GB native GGUF—smaller than an 8B model stored in FP16 and under 2.5 effective artifact bits per parameter.

On the step-160 candidate's held-out teacher-correct tool gate, BTL-3 Compact retained 62 of 63 behaviors, yielding 98.4% conditional retention.

Overview

BTL-3 Compact is the portable native edition of BTL-3. It packages the complete text model—including the compressed decoder, vocabulary matrices, output correction, and behavior repair—into a single 8,392,369,600-byte GGUF.

The runtime consumes the packed representation directly. It does not download, load, or reconstruct the original BF16 checkpoint.

Highlights

• Complete Qwen3.6-27B-derived text model in 8.39 GB decimal / 7.82 GiB.
• Full 64-layer mixed AVQ2/UniSVQ representation with targeted precision islands and compact behavioral corrections.
• 98.4% conditional retention (62/63) on the step-160 candidate's held-out teacher-correct tool gate.
• Exact native CUDA and Metal execution with no persistent dense reconstruction.
• OpenAI-compatible local server plus LM Studio and Ollama CLI integrations.
• Verified macOS arm64 package and a clearly separated DGX Spark CUDA preview.

Model specification

Usable context depends on device memory, KV cache, and runtime workspace. Start with 4K on a 16 GB Mac, 16K on a 12–16 GB GPU, or 32K on a 24 GB GPU, then increase the window after measuring headroom.

Measured retention

The candidate gate contains 63 turns across single, parallel, sequential, parallel-multiple, and abstention behavior. It is an internal contract-retention evaluation, not a public frontier benchmark.

The complete candidate report is included in evidence/thinking-escape-v3-tool-gate.json.

Native performance

The RTX result is the mean of three native runs with a 512-token prompt and 128 generated tokens. The M2 measurement is a compatibility smoke on the entry-level 16 GB system, not a performance projection for newer Apple hardware.

Runtime support

Stock Ollama and the stock LM Studio GGUF engine do not decode AVQ2 directly. The included integrations start or connect to BTL's native runner while preserving the familiar client interface.

Quickstart

Current reasoning-policy note: BTL-3 Compact is released with thinking disabled by default. Use the supported non-thinking path for chat, coding, and tools. The experimental thinking override is currently discouraged because it can repeat procedural reasoning or fail to terminate. On the step-160 held-out smoke, 5/12 prompts reached </think> and a final answer; thinking-enabled coding reached 0/3. The default no-thinking path passed direct-answer stopping and executable Python validation on the exact GGUF.

Install the verified macOS package

python3 tools/install_consumer_bundle.py \
  --runtime runtimes/supported/BTL-3-Compact-macos-arm64 \
  --model model/BTL-3-Compact-AVQ2.gguf

Start the server directly

BTL3_MODEL="$PWD/model/BTL-3-Compact-AVQ2.gguf" \
BTL3_CTX_SIZE=4096 \
  runtimes/supported/BTL-3-Compact-macos-arm64/bin/btl3-server

Call the OpenAI-compatible API

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "BTL-3",
    "messages": [
      {
        "role": "user",
        "content": "Write a retrying fetch helper and include tests."
      }
    ],
    "chat_template_kwargs": {"enable_thinking": false},
    "stream": true
  }'

Representation

The release combines:

• packed AVQ2 decoder tensors;
• affine INT4 tensors and two measured INT4 demotions;
• selected higher-precision islands;
• packed embedding and output matrices;
• a rank-32 output correction;
• a compact behavior adapter.

All 2,416 tensor payloads were byte-verified during export. Native CUDA parity was checked against the reference representation, and the exact exported GGUF subsequently completed autoregressive generation on CUDA and Metal.

Artifact integrity

Verify the complete release directory:

shasum -a 256 -c SHA256SUMS

RELEASE_MANIFEST.json records the exact model identity and separates verified runtimes from preview packages.

Intended use

• local coding and debugging;
• private repository and terminal agents;
• structured single, sequential, and parallel tool use;
• offline or self-hosted OpenAI-compatible inference;
• consumer and workstation deployments where the BF16 model is impractical.

Operational guidance

Run generated code and tool calls in a sandbox. Require explicit confirmation before destructive, privileged, financial, or otherwise high-impact actions.

License and citation

The model artifact is Apache-2.0. The included runtime is MIT-licensed; see the packaged licenses and third-party notices.

@software{btl3_compact_2026,
  title  = {BTL-3 Compact: A Native 8.39 GB Agentic Coding Model},
  author = {Bad Theory Labs},
  year   = {2026},
  url    = {https://huggingface.co/badtheorylabs/BTL-3-Compact}
}

For questions and release updates, visit Bad Theory Labs or join the community Discord.