--- license: apache-2.0 base_model: Qwen/Qwen3.6-27B pipeline_tag: text-generation library_name: llama.cpp tags: - agent - coding - reasoning - tool-use - function-calling - gguf - quantized - cuda - metal ---
# 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](https://huggingface.co/badtheorylabs/BTL-3) · [Runtime source](https://github.com/Badtheorylabs/BTL-3) · [Bad Theory Labs](https://www.badtheorylabs.com/) · [Discord](https://discord.gg/QJBCcB7bF)
> **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 | Item | Specification | |---|---| | Model lineage | Qwen3.6-27B → BTL-3 RL-0013 | | Scope | Text-only coding, reasoning, and tool use | | Layers | 64 | | Model file | `model/BTL-3-Compact-AVQ2.gguf` | | Deployed model bytes | 8,392,369,600 | | Deployed model size | 8.39 GB / 7.82 GiB | | Architectural context | 262,144 tokens | | Packed tensors | 2,416 | | Runtime | BTL packed llama.cpp | | License | Apache-2.0 model, MIT runtime | 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. | Metric | Result | |---|---:| | Teacher-correct cases | **63/63** | | BTL-3 Compact | **62/63** | | Conditional retention | **98.4% (62/63)** | | Single-call retention | **100% (23/23)** | | Parallel-call retention | **100% (14/14)** | | Parallel-multiple retention | **100% (2/2)** | | Sequential-call retention | **87.5% (7/8)** | | Abstention retention | **100% (16/16)** | The complete candidate report is included in [`evidence/thinking-escape-v3-tool-gate.json`](evidence/thinking-escape-v3-tool-gate.json). ## Native performance | Device | Prompt processing | Generation | Runtime status | |---|---:|---:|---| | RTX PRO 6000 Blackwell 96 GB | **84.70 tok/s** | **43.16 tok/s** | Exact GGUF, full CUDA offload | | Apple M2 16 GB | **2.30 tok/s** | **2.48 tok/s** | Exact GGUF, Metal compatibility smoke | 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 | Target | Status | Package | |---|---|---| | macOS arm64 / Apple Metal | **Verified** | `runtimes/supported/BTL-3-Compact-macos-arm64` | | Linux arm64 / NVIDIA CUDA / DGX Spark | Preview | `runtimes/preview/BTL-3-Compact-linux-arm64-cuda` | | OpenAI-compatible HTTP | **Verified** | Native `btl3-server` | | LM Studio | **Supported through included generator** | `integrations/btl3-native` | | Ollama CLI | **Supported through included bridge** | `integrations/ollama` | 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 `` 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 ```bash 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 ```bash 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 ```bash 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 | Artifact | Value | |---|---| | File | `model/BTL-3-Compact-AVQ2.gguf` | | Bytes | `8,392,369,600` | | SHA-256 | `0a4d9ddee49e5aa93586a792bd4d452ea837229d49d22e54212dde87a5c9888a` | Verify the complete release directory: ```bash 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. ```bibtex @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](https://www.badtheorylabs.com/) or join the [community Discord](https://discord.gg/QJBCcB7bF).