Text Generation
GGUF
llama.cpp
agent
coding
reasoning
tool-use
function-calling
quantized
cuda
metal
conversational
Instructions to use badtheorylabs/BTL-3-Compact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 5,065 Bytes
274ffba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | """Install a verified BTL-3 consumer runtime and its external model."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
from pathlib import Path
import platform
import shutil
import tempfile
MODEL_NAME = "BTL-3-Compact-AVQ2.gguf"
MODEL_BYTES = 8_392_369_600
MODEL_SHA256 = "2ddf9527620a17a2a6739d184a7096c45712092e6589128792ec6254e94dc30c"
class InstallError(ValueError):
pass
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for chunk in iter(lambda: stream.read(4 * 1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def default_prefix() -> Path:
if os.name == "nt":
root = os.environ.get("LOCALAPPDATA")
if not root:
raise InstallError("LOCALAPPDATA is unavailable; pass --prefix")
return Path(root) / "BTL3"
root = os.environ.get("XDG_DATA_HOME")
return Path(root) / "btl3" if root else Path.home() / ".local/share/btl3"
def read_manifest(runtime: Path) -> dict:
path = runtime / "bundle-manifest.json"
try:
manifest = json.loads(path.read_text())
except (OSError, json.JSONDecodeError) as error:
raise InstallError(f"invalid runtime manifest: {error}") from error
external = manifest.get("external_model", {})
expected = {
"filename": MODEL_NAME,
"bytes": MODEL_BYTES,
"sha256": MODEL_SHA256,
}
if any(external.get(key) != value for key, value in expected.items()):
raise InstallError("runtime expects a different BTL-3 model")
return manifest
def verify_runtime(runtime: Path, manifest: dict) -> None:
for name, expected in manifest.get("files", {}).items():
path = runtime / name
if "symlink" in expected:
if not path.is_symlink() or os.readlink(path) != expected["symlink"]:
raise InstallError(f"runtime symlink mismatch: {name}")
continue
if not path.is_file():
raise InstallError(f"runtime file is missing: {name}")
if path.stat().st_size != expected["bytes"]:
raise InstallError(f"runtime file size mismatch: {name}")
if sha256(path) != expected["sha256"]:
raise InstallError(f"runtime file checksum mismatch: {name}")
def verify_model(model: Path) -> None:
if not model.is_file():
raise InstallError(f"model is missing: {model}")
if model.stat().st_size != MODEL_BYTES:
raise InstallError(f"model size mismatch: {model.stat().st_size}")
if sha256(model) != MODEL_SHA256:
raise InstallError("model SHA-256 mismatch")
def install(runtime: Path, model: Path, prefix: Path, replace: bool) -> Path:
runtime, model, prefix = runtime.resolve(), model.resolve(), prefix.resolve()
manifest = read_manifest(runtime)
verify_runtime(runtime, manifest)
verify_model(model)
if prefix.exists() and not replace:
raise InstallError(f"install already exists (use --replace): {prefix}")
prefix.parent.mkdir(parents=True, exist_ok=True)
staging = Path(tempfile.mkdtemp(prefix=f".{prefix.name}-", dir=prefix.parent))
try:
shutil.copytree(runtime, staging, dirs_exist_ok=True, symlinks=True)
destination = staging / "model" / MODEL_NAME
destination.parent.mkdir(parents=True, exist_ok=True)
shutil.copy2(model, destination)
receipt = {
"schema_version": 1,
"installed_by": "BTL-3 consumer installer",
"platform": platform.platform(),
"runtime_target": manifest.get("target") or manifest.get("platform"),
"model": {
"path": f"model/{MODEL_NAME}",
"bytes": MODEL_BYTES,
"sha256": MODEL_SHA256,
},
}
(staging / "install-receipt.json").write_text(
json.dumps(receipt, indent=2) + "\n"
)
if prefix.exists():
backup = prefix.with_name(f".{prefix.name}.previous")
shutil.rmtree(backup, ignore_errors=True)
prefix.rename(backup)
try:
staging.rename(prefix)
except Exception:
backup.rename(prefix)
raise
shutil.rmtree(backup)
else:
staging.rename(prefix)
except Exception:
shutil.rmtree(staging, ignore_errors=True)
raise
return prefix
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--runtime", type=Path, required=True)
parser.add_argument("--model", type=Path, required=True)
parser.add_argument("--prefix", type=Path, default=None)
parser.add_argument("--replace", action="store_true")
args = parser.parse_args()
try:
print(install(
args.runtime,
args.model,
args.prefix or default_prefix(),
args.replace,
))
except InstallError as error:
parser.error(str(error))
if __name__ == "__main__":
main()
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