Instructions to use AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP 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("AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP")

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 AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP 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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP 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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP"

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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP 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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP"

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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP" \
  --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-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP

Re-conversion notice (2026-09-23)

This artifact was re-converted on the AXQuant factory host with AXQuant 1.9.0 (commit 723eaeb1fb5b), which preserves the exact source tokenizer assets inside the converter and excludes the grafted MTP shard from the MLX-LM text-path view during requantization. Conversion integrity checks (byte-exact MTP/vision sidecars, tokenizer byte equality, greedy text smoke) were re-executed for this revision.

The previous revision (5ab39b24bfd7f65203be9b7823b1840486f58b6d) remains available in this repository's history, but users should re-download the latest revision of this model to get this build. The disclosures below still apply: this is a development repack, not a certified artifact.

Metadata correction (same day, follow-up revision): the packaged axquant_plan.json and axquant_manifest.json target_class was corrected from the allocator-inherited 8bit label to the actual product class MXFP4. Weight payloads are byte-identical to the first re-conversion revision; only those two metadata files (and this notice) changed.

Development repack. Not certified. No measured quality-parity or MTP-speed claim.

This is an AXQuant MXFP4 requantization of the exact peculiar-ragdoll/Tiel-Coder-35B-A3B-MLX-oQ6e-MTP checkpoint, revision 88625754ac91b542280a5602239ce6b2166366f0. The source is already mixed-precision oQ6e, not original BF16. Public MLX APIs dequantize its weights and repack the language trunk using an AXQuant manual recipe. This adds quantization error and does not restore the original full-precision weights. Upstream benchmark results and coding-performance claims do not apply to this repack. No upstream importance matrix, calibration corpus, or quantizer implementation is imported.

Artifact

Property	Value
AXQuant version	1.9.0
AXQuant commit	`723eaeb1fb5b`
Architecture	Qwen3.5-class 35B-A3B MoE; qwen35-moe-v1
Language trunk	Native MLX MXFP4, group size 32
Protection floors	Embeddings/routers at least 8-bit; norms/LM head BF16
Measured main BPW	4.634223
Measured total BPW	4.901270
Weight bytes	22026200211
MTP	785 source tensors preserved in mtp.safetensors
Vision	Source vision tensors preserved in vision.safetensors
Previous revision	`5ab39b24bfd7f65203be9b7823b1840486f58b6d`
Certification	None; architecture-prior manual allocation

The protected MTP and vision tensor payloads were compared byte-for-byte with the pinned source. The source tokenizer and Sharp chat template are preserved. Basic deterministic text-generation smoke tests ran with MLX-LM on the factory host. These checks establish conversion integrity and basic loadability, not model quality, vision capability, MTP acceptance, or acceleration. AX Engine execution was not measured for this artifact in this campaign.

Download and basic text generation

python -m pip install mlx-lm huggingface_hub
hf download AutomatosX/AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP --local-dir ./AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP
mlx_lm.generate --model ./AX-Tiel-Coder-35B-A3B-MLX-AXQ-MXFP4-MTP --max-tokens 256 --prompt "Write a Python add function."

Use an Apple Silicon Mac with sufficient unified memory. Pin the published Hub commit for reproducible deployments. The tested packaging environment used MLX-LM 0.31.3. MXFP4 here is the native MLX Apple format, not NVIDIA NVFP4.

MTP and multimodal scope

The -MTP suffix means the head is packaged, not that acceleration is certified or enabled by the text command above. Stock MLX-LM text generation does not use the separate MTP sidecar. A compatible sidecar-aware runtime is required, and runtime-specific MTP enablement must be validated separately. No MTP grafting or new training was performed. The upstream MTP provenance remains that of the exact source repository. Vision weights are retained but this campaign does not claim vision inference support.

Provenance and license

The upstream model card declares MIT. Follow its license terms and usage information. Credits: peculiar-ragdoll for the exact source build and template, Ornith for the model lineage, and the additional upstream contributors identified in the source model card. AXQuant uses public MLX conversion APIs. See axquant_plan.json, axquant_quantizer_execution.json, the protected-sidecar manifests, and axquant_manifest.json for allocation and file bindings.