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 / evidence /compact-validation.md

affableiq

Publish BTL-3 Compact package metadata

274ffba verified 8 days ago

preview code

Raw

History Blame Contribute Delete

4.63 kB

BTL-3 Compact native release validation

Date: 2026-07-19

Candidate

Public model: BTL-3 Compact.
Model lineage: Qwen3.6-27B plus BTL RL0013 and the frozen behavior repair.
Scope: text-only coding, tool use and agent behavior.
Representation: full 64-layer AVQ2/UniSVQ decoder, two measured INT4 demotions, selected BF16 islands, packed vocabulary matrices, rank-32 head correction and a small behavior adapter.
Source payload bytes before GGUF packing: 8,572,070,080.
Source weight bytes: 8,551,772,952.
Portable GGUF bytes: 8,392,369,600.
Portable GGUF SHA-256: 2ddf9527620a17a2a6739d184a7096c45712092e6589128792ec6254e94dc30c.

The package is complete enough to instantiate and generate without downloading or loading the BF16 Qwen checkpoint.

Native runtime proof

The standalone loader meta-initializes the Qwen3.6 text architecture and installs only the package's small state, packed decoder tensors, packed embedding, packed head, rank-32 output correction and behavior LoRA.

The H100 smoke test observed:

no surviving dense compatible decoder matrices;
exact AVQ2 CUDA-kernel parity with the unpacked reference;
INT4 maximum absolute kernel error of 3.0517578125e-05;
standalone model peak CUDA allocation of 8,552,500,736 bytes;
successful autoregressive generation.

The source payload was subsequently exported into the portable GGUF without reconstructing dense weights. The exporter byte-verified all 2,416 payloads and reported no unsupported tensors or native runtime gaps. The exact GGUF then passed native llama.cpp generation on Apple Metal and NVIDIA CUDA. MLX, WebGPU, phone execution, and stock-engine compatibility remain unverified.

Fresh sealed gate

Benchmark ID: btl-fresh-tool-gate-2026-07-19-v1

Cases SHA-256: d656a7862e16e64ed3a359ba1de10f7eafefad77f6cf5a8264d60287e1890a45

The gate was authored after compression and behavior-repair choices were frozen. It contains 100 scored turns:

20 single calls;
20 parallel calls;
20 sequential calls;
20 parallel-multiple calls;
20 abstention decisions.

All tool families are first-party and use a new lumenharbor_* namespace. Mechanical QA found no schema errors, duplicate IDs, parse errors or exact tool name overlap with repository training/evaluation data.

This is a private synthetic contract-retention gate. It is not a public coding benchmark and must not be presented as a frontier benchmark score.

Full-precision teacher

The frozen RL0013 teacher scored:

Category	Correct	Total
Single	20	20
Parallel	20	20
Sequential	20	20
Parallel-multiple	10	20
Abstention	20	20
Overall	90	100

The release metric is conditional retention on these 90 teacher-correct turns, reported separately for every category and overall. Absolute student accuracy is also retained in the raw result.

Standalone result

The standalone package scored:

Category	Student correct	Total	Teacher-correct retained	Retention
Single	20	20	20 / 20	100%
Parallel	20	20	20 / 20	100%
Sequential	20	20	20 / 20	100%
Parallel-multiple	3	20	3 / 10	30%
Abstention	20	20	20 / 20	100%
Overall	83	100	83 / 90	92.2%

All generations stopped. The measured malformed rate was 7%, entirely within the difficult parallel-multiple family.

The seven teacher-correct/student-wrong parallel-multiple cases were not parser false negatives. The package emitted a fluent abstention instead of making the three requested independent calls. This is a real over-abstention failure under novel multi-call schemas.

Decision

The package passes a 90% overall conditional-retention rule. It fails a 90% per-category rule because parallel-multiple retention is 30%.

Do not alter compression based on this sealed result. Any behavior repair aimed at these cases creates a new candidate and requires a newly authored, untouched release gate.

Release boundary

This result validates that the exact text-only source payload is physically standalone and retains more than 90% overall on the fresh CUDA gate. The GGUF export preserved those payload bytes exactly. It does not support a claim of uniformly preserved behavior or phone deployment.

The release includes a distributable macOS native runtime and exact-artifact throughput measurements on Apple M2 and RTX PRO 6000. Other GPU packages, stock Ollama/LM Studio execution, mobile runtimes, and a public compact-specific coding benchmark remain separate gates.