Instructions to use mlx-community/Tmax-9B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-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/Tmax-9B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Tmax-9B-MLX-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/Tmax-9B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Tmax-9B-MLX-4bit / README.md

Raullen

bench: correct 27B prefill_16k numbers (false hang ruled out, hybrid attention narrative)

62a9063 verified 16 days ago

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	---
	license: apache-2.0
	library_name: mlx
	base_model: allenai/tmax-9b
	tags:
	- mlx
	- qwen3_5
	- text-generation
	pipeline_tag: text-generation
	---

	# Tmax-9B MLX (4bit)

	MLX-converted text-only weights of [`allenai/tmax-9b`](https://huggingface.co/allenai/tmax-9b).

	The upstream base ships as a multimodal `Qwen3_5ForConditionalGeneration`
	config but contains zero vision tensors in its safetensors — i.e. it is
	already a text-only checkpoint with stub vision metadata. This release
	strips the residual `vision_config` / image-token entries so it loads
	cleanly via `mlx_lm` without a vision tower.

	- Source: `allenai/tmax-9b`
	- License: Apache-2.0
	- Variant: `4bit`
	- Quantized by: raullenchai
	- Tooling: `mlx-lm 0.31.3` (the upstream `mlx_vlm 0.3.12` qwen3_5
	loader hard-requires vision-tower weights that this base does not ship,
	so the text-only `mlx_lm.convert` path is used instead)
	- Chat template: ships with the source repo (`chat_template.jinja`)
	- Tool format: `qwen3_xml`-compatible (`<tool_call>{json}</tool_call>`)

	## Usage

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("mlx-community/Tmax-9B-MLX-4bit")
	print(generate(model, tokenizer, prompt="Hello", max_tokens=32))
	```

	## Notes

	- This is a pure text-generation MLX release. No vision/image inputs.
	- For best chat behavior, use the chat template that ships with this repo.

	## Benchmarks

	> Measured on M3 Ultra Studio (28 (20 Performance and 8 Efficiency) CPU, 60-core GPU, 256 GB unified memory) via rapid-mlx 0.8.18. Medians of 3 runs.

	\| Variant \| Decode tok/s \| TTFT (ms) \| Prefill 1k (tok/s) \| Prefill 4k (tok/s) \| Prefill 16k (tok/s) \| Tool-call e2e \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| Tmax-9B (4-bit MLX) \| 107.4 \| 127 \| 1,060 \| 1,124 \| 1,092 \| 726 ms (OK) \|

	Recommended default for the 9B family on M3 Ultra — ~19% faster decode than the Qwen3.5-9B-4bit control on the same hardware (90.5 tok/s), tool-call e2e under 1 s.

	Full results (all 7 Tmax MLX variants + 2 Qwen3.5 controls): [rapid-mlx docs](https://github.com/raullenchai/Rapid-MLX/blob/main/docs/benchmarks/tmax-m3-ultra.md).

	Reproduce:

	```bash
	pip install rapid-mlx==0.8.18
	rapid-mlx serve tmax-9b --port 8765
	```