Instructions to use AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit") config = load_config("AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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 "AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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": "AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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 "AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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 AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit
Run Hermes
hermes
- OpenClaw new
How to use AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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 "AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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 "AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-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"
AX Devstral Small 2 24B Instruct 2512 MLX OptiQ 4-bit
Parameter count: 24,011,361,280 logical inference parameters (approximately 24.01B).
4-bitnames the low precision tier; it is not a 4B model-size claim. The count includes the bundled BF16 vision tower.
The logical count is derived from every tensor shape in the pinned BF16 source and independently reconstructed from the packed MLX artifact. It intentionally does not treat packed storage elements—or an inconsistent upstream index override—as the architecture size.
This is an MLX OptiQ mixed-precision release of a multimodal agentic coding and software-engineering model for Apple Silicon. It is intended for Apple Silicon and retains image-and-text support through an indexed BF16 vision sidecar.
Try this model locally with AX Engine.
Attribution and changes
AutomatosX did not train the base model. AutomatosX produced this OptiQ
conversion from mlx-community/Devstral-Small-2-24B-Instruct-2512-bf16
at immutable revision 5cc5ba993ad5220ec4cbac1ab7126cd80189094a with
mlx-optiq 0.4.2.
The conversion used exact calibration-driven per-layer
KL sensitivity against the BF16
reference: 24 sequences of 512 tokens from the six-domain OptiQ mix,
4/8-bit candidates, group size 64, and a target of 5.0 bits per weight. The
target and achieved BPW values describe OptiQ's measured language allocation,
not the complete download. The Mistral tokenizer config also sets
fix_mistral_regex: true, following the runtime warning for correct token
splitting. No model training or benchmark results are claimed by AutomatosX.
The source model card is preserved as UPSTREAM_README.md; exact conversion
inputs, allocation, validation, and hashes are recorded in
ax_provenance.json, optiq_metadata.json, and optiq_sensitivity.json.
The source index mapping matched the actual shards; it was rebuilt from the Safetensors headers to add verified logical-count metadata. The verbatim upstream and rebuilt source indexes are preserved as UPSTREAM_MODEL_SAFETENSORS_INDEX.json and REBUILT_SOURCE_MODEL_SAFETENSORS_INDEX.json.
The pinned MLX source config contains max_position_embeddings: 393,216, while Mistral's official model card documents 262,144 tokens. This release preserves the source config value in text_config and keeps generation_config.max_length at 262,144. Treat 262,144 tokens as the documented operational context unless you have validated a longer window yourself.
Package details
| Property | Value |
|---|---|
| Format | MLX Safetensors |
| Quantization | OptiQ mixed 4/8-bit, group size 64 |
| Measured allocation | 112 components at 4-bit; 169 at 8-bit |
| Allocation target / achieved BPW | 5.0 / 5.1053 |
| Sensitivity reference | BF16 |
| Calibration | 24 × 512-token, six-domain mix |
| Allocation guards | 2-component block floor; 3-block low-bit run limit |
| Vision tower | Bundled BF16 sidecar |
| Source companions | processor_config.json, params.json, tekken.json, CHAT_SYSTEM_PROMPT.txt, VIBE_SYSTEM_PROMPT.txt |
| Official documented context | 262,144 tokens |
Source config max_position_embeddings |
393,216 tokens |
| Logical parameters | 24,011,361,280 (24.01B) |
Original upstream index total_parameters |
Not provided |
| Download size | Approximately 17.7 GB |
| Intended hardware | Apple Silicon |
Measured allocation counts OptiQ's sensitivity-selected language components
only. Forced high-bit tensors outside that set (notably
language_model.model.embed_tokens at 8-bit) appear in config.json and can
make a naive config recount differ by one 8-bit entry.
Achieved BPW is above target because allocation guards (first/last protection, block floor, and low-bit run limit) keep additional language components at 8-bit.
The download size also includes the BF16 vision sidecar, protected or
unmeasured high-precision components, quantization scales/biases, Safetensors
headers, tokenizer assets (including tekken.json when bundled), processor
config, and documentation. It therefore should not be estimated by multiplying
the logical parameter count by the achieved BPW.
Recommended system prompts
This package includes upstream agent prompts from the pinned BF16 source:
CHAT_SYSTEM_PROMPT.txt— general Le Chat-style assistantVIBE_SYSTEM_PROMPT.txt— Mistral Vibe coding-agent wrapper
Pass the relevant file contents as the system message for agentic or coding use.
Run with MLX-VLM
pip install -U mlx-vlm
python -m mlx_vlm.generate \
--model AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit \
--max-tokens 100 \
--temperature 0 \
--prompt "Describe this image." \
--image <path_to_image>
Download and serve with AX Engine
hf download AutomatosX/AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit \
--local-dir ./AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit
ax-engine doctor \
--mlx-model-artifacts-dir ./AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit
ax-engine serve ./AX-Devstral-Small-2-24B-Instruct-2512-MLX-OptiQ-4bit --port 31418
Validation
Validated on macOS arm64 with MLX 0.32.0, MLX-LM 0.31.3, MLX-VLM 0.6.6, mlx-optiq 0.4.2, and AX Engine 6.11.0:
- all JSON and Safetensors index mappings: passed
- stock MLX-LM text load/generation:
READY - MLX-VLM image load/generation through the BF16 sidecar: passed
- AX native manifest validation and artifact doctor:
ready - Hub logical parameter override: 24,011,361,280
Quantization can change model quality. Evaluate the model on your own tasks, especially long-context, coding, multilingual, tool-use, and vision workloads.
License
Apache License 2.0. See LICENSE, the
mistralai/Devstral-Small-2-24B-Instruct-2512 model card,
and UPSTREAM_README.md for limitations and responsible-use guidance.
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4-bit
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Base model
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