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 / docs /patched-ollama-and-lmstudio.md

affableiq

Publish BTL-3 Compact package metadata

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BTL-3 consumer integrations

This document separates three products that must not be conflated:

Surface	How BTL-3 executes	Current status
Stock Ollama	Stock runner	Does not execute AVQ2
BTL-3 Patched Ollama	Ollama directly spawns BTL's native `llama-server`	Packaging scaffold complete; target packages pending
LM Studio	Official generator plugin calls BTL's native local endpoint	Plugin type-checks; macOS native endpoint verified

No HTTP translation bridge sits between patched Ollama and the runner.

Why the runner replacement is small

At official Ollama commit 573386c35eac76124ffce571f4b0fefa0a7fe13c, llm.NewLlamaServer delegates every GGUF model to a llama-server subprocess. Ollama locates that executable under lib/ollama, starts it on an ephemeral loopback port, and speaks its native /health, /completion, /v1/chat/completions, tokenization, and embedding endpoints. The relevant upstream files are llm/server.go, llm/llama_server.go, and llm/llama_binary.go.

BTL's native runner already exposes that contract. The patched distribution replaces Ollama's runtime payload, not its public API, scheduler, CLI, model store, or UI.

Build the CUDA runner payload

Build on the target machine so CMake selects the target GPU:

cmake -S native/llama.cpp -B native/llama.cpp/build-btl3-cuda \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CUDA_ARCHITECTURES=native \
  -DBUILD_SHARED_LIBS=ON \
  -DGGML_BACKEND_DL=ON \
  -DGGML_CUDA=ON \
  -DLLAMA_CURL=OFF
cmake --build native/llama.cpp/build-btl3-cuda \
  --target llama-server --parallel

Stage the executable and its shared libraries. Use cuda_v13 with CUDA 13 and cuda_v12 with CUDA 12:

payload=/tmp/btl3-runner
cuda_dir=cuda_v13
rm -rf "$payload"
mkdir -p "$payload/$cuda_dir"
cp native/llama.cpp/build-btl3-cuda/bin/llama-server "$payload/"
cp native/llama.cpp/build-btl3-cuda/bin/libggml*.so* "$payload/"
cp native/llama.cpp/build-btl3-cuda/bin/libllama*.so* "$payload/"
mv "$payload"/libggml-cuda.so* "$payload/$cuda_dir/"
"$payload/llama-server" --help

Do not package until --help exposes --model, --host, --port, --no-webui, --offline, and -np. The packager enforces that contract.

Hardware targets

RTX 4090: build linux-amd64-cuda12 or cuda13 on that machine.
RTX 5090: build on the 5090 so native includes its Blackwell target.
Windows RTX 4090/5090: build windows-amd64-cuda13 on Windows x64.
DGX Spark: build linux-arm64-cuda13 on Spark. Current DGX Spark software is ARM64 and ships CUDA 13; do not reuse an x86_64 payload. See NVIDIA's hardware overview and current release notes.

Build BTL-3 Patched Ollama

Build Ollama from the pinned source commit using its official Linux build, or unpack a distribution built from that exact commit into OLLAMA_DIST. Then:

.venv/bin/python tools/build_patched_ollama.py \
  --ollama-dist "$OLLAMA_DIST" \
  --runner-payload /tmp/btl3-runner \
  --platform linux-amd64-cuda13 \
  --output dist/btl3-patched-ollama-linux-amd64-cuda13

The output contains:

bin/ollama
bin/btl3-ollama
lib/ollama/llama-server
lib/ollama/cuda_v13/libggml-cuda.so
share/btl3/patched-ollama-manifest.json

btl3-ollama --btl3-info prints the patched label. All other arguments are passed to the packaged Ollama binary.

Create the local model only with the patched distribution:

cp artifacts/release/BTL-3-Compact-AVQ2.gguf integrations/ollama/
dist/btl3-patched-ollama-linux-amd64-cuda13/bin/btl3-ollama serve
# in another terminal
dist/btl3-patched-ollama-linux-amd64-cuda13/bin/btl3-ollama \
  create btl3-compact -f integrations/ollama/Modelfile
dist/btl3-patched-ollama-linux-amd64-cuda13/bin/btl3-ollama \
  run btl3-compact

The GGUF stores BTL's packed representation in ordinary I8/F32/BF16 tensors with custom names, so no new GGUF tensor-type enum is required. Actual model creation and generation remain release gates on each CUDA target.

Windows x64 CUDA 13

The pinned Ollama source has the same direct subprocess architecture on Windows. Its llama_binary.go adds .exe and searches beside a top-level ollama.exe at lib\ollama\llama-server.exe; the pinned build_windows.ps1 installs CUDA payloads under that same directory.

Build the native runner from an x64 Visual Studio developer shell with CUDA 13:

cmake -S native/llama.cpp -B native/llama.cpp/build-btl3-win-cuda13 `
  -A x64 `
  -DBUILD_SHARED_LIBS=ON `
  -DGGML_BACKEND_DL=ON `
  -DGGML_CUDA=ON `
  -DLLAMA_CURL=OFF
cmake --build native/llama.cpp/build-btl3-win-cuda13 `
  --config Release --target llama-server --parallel

$Bin = "native/llama.cpp/build-btl3-win-cuda13/bin/Release"
$Payload = "$env:TEMP/btl3-runner-win"
Remove-Item -Recurse -Force $Payload -ErrorAction SilentlyContinue
New-Item -ItemType Directory "$Payload/cuda_v13" | Out-Null
Copy-Item "$Bin/llama-server.exe" $Payload
Copy-Item "$Bin/*.dll" $Payload
Move-Item "$Payload/ggml-cuda.dll" "$Payload/cuda_v13/"
powershell -ExecutionPolicy Bypass -File tools/probe_windows_runner.ps1 `
  -Runner "$Payload/llama-server.exe" `
  -Output "$Payload/runner-cli-contract.json"

The target-side probe executes the exact runner and binds its required Ollama CLI flags to its SHA-256. Cross-platform packaging refuses a missing, stale, or wrong-platform contract. It also checks that ollama.exe, llama-server.exe, and every staged DLL are PE x64.

Package an official pinned Windows x64 distribution:

python tools/build_patched_ollama.py `
  --ollama-dist "$env:TEMP/ollama-windows-amd64" `
  --runner-payload "$env:TEMP/btl3-runner-win" `
  --platform windows-amd64-cuda13 `
  --output dist/btl3-patched-ollama-windows-amd64-cuda13

dist\btl3-patched-ollama-windows-amd64-cuda13\btl3-ollama.cmd --btl3-info
dist\btl3-patched-ollama-windows-amd64-cuda13\btl3-ollama.cmd serve

The Windows output uses top-level ollama.exe and btl3-ollama.cmd, with the runner at lib\ollama\llama-server.exe and CUDA backend at lib\ollama\cuda_v13\ggml-cuda.dll. This path is source-verified and package-tested, but still requires generation conformance on real Windows CUDA hardware before release.

Required Ollama release gates

For each 4090, 5090, and DGX Spark artifact:

Verify runner and CUDA library architecture with file/ldd on Linux, or dumpbin /headers and /dependents on Windows.
Run btl3-ollama --btl3-info and verify the manifest hashes.
Create the model in a temporary OLLAMA_MODELS directory.
Confirm Ollama itself spawns the packaged runner PID.
Exercise streaming chat, cancellation, reasoning, single and parallel tool calls, JSON schema output, context reuse, and unload/reload.
Reverify the exact model SHA-256: 2ddf9527620a17a2a6739d184a7096c45712092e6589128792ec6254e94dc30c.

Until those checks pass on a real CUDA executable, the output is a packaging candidate—not an Ollama-compatible release.

LM Studio generator

LM Studio's official plugin API treats generators as replacement token sources. The BTL plugin therefore calls the native runner's OpenAI-compatible endpoint; it does not ask LM Studio's stock engine to interpret AVQ2. Official generator and tool-call APIs are documented in LM Studio's generator introduction and tool-calling guide.

Install the BTL native runner and model into the platform-default BTL-3 data directory (or configure their paths in the plugin), then:

cd integrations/lmstudio/btl3-native
./run-local

The script installs the locked dependencies and runs the official lms dev workflow. The model appears in LM Studio's model picker as the generator plugin. Its default endpoint is http://127.0.0.1:8080/v1. When that endpoint is offline, the Node plugin starts the installed native runner directly with the configured model; no separately launched compatibility server is required.

The plugin preserves:

streamed content;
reasoning fragments through reasoningType: "reasoning";
cancellation through ctl.abortSignal;
full chat history and tool results;
parallel tool-call IDs, names, and JSON argument fragments;
explicit propagation of cancellation, connection, and API failures.

Content and reasoning stream live. LM Studio's generator event lifecycle represents one active tool call at a time and its argument-fragment callback does not include a call ID. To preserve BTL-3's parallel calls correctly, the plugin buffers tool fragments by upstream call index and emits each complete lifecycle sequentially when the server stream ends. It does not misattribute interleaved arguments merely to make the tool UI look live.

The included model.yaml is catalog metadata, not a claim that the stock LM Studio GGUF engine supports AVQ2. It uses the explicit compatibility label btl3-avq2-native.