Instructions to use badtheorylabs/BTL-3-Compact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use badtheorylabs/BTL-3-Compact with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="badtheorylabs/BTL-3-Compact",
	filename="model/BTL-3-Compact-AVQ2.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use badtheorylabs/BTL-3-Compact with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf badtheorylabs/BTL-3-Compact
# Run inference directly in the terminal:
llama cli -hf badtheorylabs/BTL-3-Compact

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf badtheorylabs/BTL-3-Compact
# Run inference directly in the terminal:
llama cli -hf badtheorylabs/BTL-3-Compact

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf badtheorylabs/BTL-3-Compact
# Run inference directly in the terminal:
./llama-cli -hf badtheorylabs/BTL-3-Compact

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf badtheorylabs/BTL-3-Compact
# Run inference directly in the terminal:
./build/bin/llama-cli -hf badtheorylabs/BTL-3-Compact

Use Docker

docker model run hf.co/badtheorylabs/BTL-3-Compact

LM Studio
Jan

vLLM

How to use badtheorylabs/BTL-3-Compact with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "badtheorylabs/BTL-3-Compact"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "badtheorylabs/BTL-3-Compact",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/badtheorylabs/BTL-3-Compact

Ollama
How to use badtheorylabs/BTL-3-Compact with Ollama:
```
ollama run hf.co/badtheorylabs/BTL-3-Compact
```

Unsloth Studio

How to use badtheorylabs/BTL-3-Compact with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for badtheorylabs/BTL-3-Compact to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for badtheorylabs/BTL-3-Compact to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for badtheorylabs/BTL-3-Compact to start chatting

How to use badtheorylabs/BTL-3-Compact with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf badtheorylabs/BTL-3-Compact

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "badtheorylabs/BTL-3-Compact"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use badtheorylabs/BTL-3-Compact with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf badtheorylabs/BTL-3-Compact

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default badtheorylabs/BTL-3-Compact

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use badtheorylabs/BTL-3-Compact with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf badtheorylabs/BTL-3-Compact

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "badtheorylabs/BTL-3-Compact" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use badtheorylabs/BTL-3-Compact with Docker Model Runner:
```
docker model run hf.co/badtheorylabs/BTL-3-Compact
```

Lemonade

How to use badtheorylabs/BTL-3-Compact with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull badtheorylabs/BTL-3-Compact

Run and chat with the model

lemonade run user.BTL-3-Compact-{{QUANT_TAG}}

List all available models

lemonade list

BTL-3-Compact / README.md

affableiq

Refresh canonical Compact with thinking-escape r8 step160 v3

1ccb6ee verified 4 days ago

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	---
	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
	---

	<div align="center">

	# 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)

	</div>

	> 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 `</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

	```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).