Instructions to use philipjohnbasile/hy3-demolition-mlx-lite-v1-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philipjohnbasile/hy3-demolition-mlx-lite-v1-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("philipjohnbasile/hy3-demolition-mlx-lite-v1-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 philipjohnbasile/hy3-demolition-mlx-lite-v1-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 "philipjohnbasile/hy3-demolition-mlx-lite-v1-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": "philipjohnbasile/hy3-demolition-mlx-lite-v1-mtp"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use philipjohnbasile/hy3-demolition-mlx-lite-v1-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 "philipjohnbasile/hy3-demolition-mlx-lite-v1-mtp"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "philipjohnbasile/hy3-demolition-mlx-lite-v1-mtp"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "philipjohnbasile/hy3-demolition-mlx-lite-v1-mtp",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use philipjohnbasile/hy3-demolition-mlx-lite-v1-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 "philipjohnbasile/hy3-demolition-mlx-lite-v1-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 philipjohnbasile/hy3-demolition-mlx-lite-v1-mtp

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use philipjohnbasile/hy3-demolition-mlx-lite-v1-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 "philipjohnbasile/hy3-demolition-mlx-lite-v1-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 "philipjohnbasile/hy3-demolition-mlx-lite-v1-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"

hy3-demolition-mlx-lite-v1-mtp / README.md

philipjohnbasile

Clarify release scope and connect the model portfolio

62c042d verified 3 days ago

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metadata

license: apache-2.0
base_model: tencent/Hy3
library_name: mlx
pipeline_tag: text-generation
language: en
tags:
  - mlx
  - apple-silicon
  - hy3
  - mixture-of-experts
  - mtp
  - speculative-decoding
  - mtplx

Hy3-Demolition-MLX lite-v1-mtp

Explore the model guide · All public work

Release at a glance

	This artifact
Purpose	The Lite trunk with Hy3's NextN sidecar restored for MTP experiments.
Runtime	Requires a compatible Hy3 model implementation and MTPLX runtime contract. The upstream backend shipped, but that does not qualify this artifact.
Status	Runtime verification pending; see the evidence and limits below.
Tensor download	112.57 GB (104.84 GiB) of root `.safetensors` files, including any root sidecars. This is a file-size total, not peak RAM.
Read first	Use the AR sibling for the documented generation path. No current end-to-end MTP speedup is established here.

The MTP-equipped variant of lite-v1: its fused trunk with the Hy3 NextN (Multi-Token-Prediction) sidecar grafted back on (num_nextn_predict_layers=1, num_experts=192), for self-speculative decoding on MTPLX.

Runtime status — reviewed September 10, 2026

The original recognized-backend-pending observation came from MTPLX 2.0.1. It is historical: the maintainer subsequently confirmed that the Hy3 and Qwen MTP backend work shipped in 2.1.0 through the release branch. Read the upstream shipping record.

That code shipment does not qualify this exact checkpoint. The source Hy3 implementation, runtime contract, loading, output agreement, and performance still need to be checked together. No fresh end-to-end MTP qualification of this artifact is claimed here. Upstream mlx-lm PR #1211 remained open at review.

The documented AR path is the sibling model.

How it was built

The MTP head consumes the trunk's final hidden state (hidden_size 4096, unchanged by pruning) and runs its own MoE on the global num_experts. So the base checkpoint's mtp.* sidecar grafts directly onto the fused AR trunk — no re-heal, no re-prune of the trunk.

Graft script + mtplx inspect receipts: https://github.com/PhilipJohnBasile/hy3-demolition-mlx (scripts/38_mtp_sidecar_graft.py, eval/receipts/mtplx_inspect_*.json).

Limitations

Does not run on stock mlx_lm as MTP. The fast MTP path needs the MTPLX backend; mlx-lm's own per-token self-speculative loop is ~4.7× slower than AR (measured), which is why the MTPLX batched-verify backend is the target.
Everything from the base lite-v1 card applies (quantized MoE, English/agent focus, no tool execution).
End-to-end MTP behavior is unverified until the backend loads it; recognition is structural (mtplx inspect), not a live run.

Base recipe + receipts: https://github.com/PhilipJohnBasile/hy3-demolition-mlx

Other serving applications

No current LM Studio or Ollama qualification of this artifact is recorded here. An architecture becoming available in one library does not establish support in every application. Use the AR sibling's pinned release recipe as the documented starting point.

Experimental streaming

The source project's SSD pager measurements concern an AR serving path. They do not qualify this MTP variant on a smaller-memory machine. Read the AR streaming experiment.