Instructions to use mlx-community/BTL-4-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/BTL-4-OptiQ-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/BTL-4-OptiQ-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/BTL-4-OptiQ-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/BTL-4-OptiQ-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/BTL-4-OptiQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mlx-community/BTL-4-OptiQ-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/BTL-4-OptiQ-4bit"

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 "mlx-community/BTL-4-OptiQ-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

MLX LM

How to use mlx-community/BTL-4-OptiQ-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/BTL-4-OptiQ-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/BTL-4-OptiQ-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/BTL-4-OptiQ-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/BTL-4-OptiQ-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/BTL-4-OptiQ-4bit"

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 mlx-community/BTL-4-OptiQ-4bit

Run Hermes

hermes

BTL-4-OptiQ-4bit / README.md

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	---
	license: apache-2.0
	library_name: mlx
	pipeline_tag: text-generation
	base_model: badtheorylabs/BTL-4
	tags:
	- mlx
	- optiq
	- quantized
	- 4bit
	- mixed-precision
	- moe
	- agentic
	- tool-use
	- code
	- apple-silicon
	---

	# mlx-community/BTL-4-OptiQ-4bit

	> Built with [mlx-optiq](https://mlx-optiq.com), the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. [All OptiQ quants](https://mlx-optiq.com/models) · [Docs](https://mlx-optiq.com/docs/) · [Qwen3.5 family](https://mlx-optiq.com/docs/qwen3.5)

	An OptiQ mixed-precision quant of [badtheorylabs/BTL-4](https://huggingface.co/badtheorylabs/BTL-4), a 35B agentic reasoning model built for tool use, software engineering and long-horizon agent work. 22.2 GB on disk, down from 70.2 GB at bf16.

	## What it is

	\| Property \| Value \|
	\|---\|---\|
	\| Base \| [badtheorylabs/BTL-4](https://huggingface.co/badtheorylabs/BTL-4) \|
	\| Architecture \| `qwen3_5_moe` — sparse mixture-of-experts \|
	\| Method \| OptiQ mixed-precision, per-layer bit allocation reused from the base family \|
	\| On disk \| 22.2 GB (bf16: 70.2 GB) \|

	BTL-4 keeps the architecture of the family it is derived from, so which layers tolerate fewer bits is unchanged and a fresh sensitivity sweep would only rediscover the same answer. The per-layer allocation comes from [Qwen3.5-35B-A3B-OptiQ-4bit](https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-OptiQ-4bit): 512 of 512 layers matched, with the routed experts mostly at 4-bit and attention, router and layer edges kept at 8-bit.

	No Capability Score is published for this quant. The base model's own benchmarks are on [its card](https://huggingface.co/badtheorylabs/BTL-4).

	## Run it

	```bash
	pip install mlx-optiq
	optiq serve --model mlx-community/BTL-4-OptiQ-4bit
	```

	That gives you an OpenAI and Anthropic compatible endpoint with mixed-precision KV cache, tool-call healing and prompt caching — useful for an agentic model, where a malformed tool call costs a whole turn.

	Or from Python:

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("mlx-community/BTL-4-OptiQ-4bit")
	prompt = tokenizer.apply_chat_template(
	[{"role": "user", "content": "List the files in the current directory."}],
	add_generation_prompt=True, tokenize=False,
	)
	print(generate(model, tokenizer, prompt=prompt, max_tokens=512))
	```

	## Links

	- Project website: [mlx-optiq.com](https://mlx-optiq.com/)
	- All OptiQ quants: [mlx-optiq.com/models](https://mlx-optiq.com/models)
	- PyPI: [pypi.org/project/mlx-optiq](https://pypi.org/project/mlx-optiq/)
	- Base model: [badtheorylabs/BTL-4](https://huggingface.co/badtheorylabs/BTL-4)