Instructions to use wang-yang/Agents-A1-MTPLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wang-yang/Agents-A1-MTPLX-Q4 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("wang-yang/Agents-A1-MTPLX-Q4")

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 wang-yang/Agents-A1-MTPLX-Q4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Agents-A1-MTPLX-Q4"

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": "wang-yang/Agents-A1-MTPLX-Q4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use wang-yang/Agents-A1-MTPLX-Q4 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 "wang-yang/Agents-A1-MTPLX-Q4"

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 wang-yang/Agents-A1-MTPLX-Q4

Run Hermes

hermes

OpenClaw new

How to use wang-yang/Agents-A1-MTPLX-Q4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Agents-A1-MTPLX-Q4"

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 "wang-yang/Agents-A1-MTPLX-Q4" \
  --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 wang-yang/Agents-A1-MTPLX-Q4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "wang-yang/Agents-A1-MTPLX-Q4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "wang-yang/Agents-A1-MTPLX-Q4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "wang-yang/Agents-A1-MTPLX-Q4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Agents-A1-MTPLX-Q4 / mtplx_runtime.json

wang-yang

Upload mtplx_runtime.json with huggingface_hub

bd61945 verified 12 days ago

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	{
	"arch_id": "qwen3-next-mtp",
	"artifact_role": "forge-local",
	"base_trunk": "InternScience/Agents-A1",
	"exactness_baseline": {},
	"forge_provenance": {
	"forge_inputs": {
	"mtp_source_path": "Ornith-1.0-35B-MTP-grafted",
	"trunk_path": "InternScience/Agents-A1"
	},
	"forge_recipe": {
	"body_bits": 4,
	"body_group_size": 64,
	"body_mode": "affine",
	"mtp_policy": "prequantized-int4"
	},
	"forged_at": "2026-06-30T12:00:00+0800",
	"forged_locally": true,
	"mtp_contract": {
	"base_hidden_variant": "post_norm",
	"concat_order": "embedding_hidden",
	"hidden_variant": "post_norm",
	"mtp_position_mode": "local",
	"mtp_quant_bits": 4,
	"mtp_quant_group_size": 64,
	"mtp_quant_mode": "affine"
	},
	"mtplx_version": "1.0.4",
	"published_to_hf": "wang-yang/Agents-A1-MTPLX-Q4",
	"source_format": "mlx_affine_with_mtp",
	"source_repo": "InternScience/Agents-A1",
	"source_sha": null
	},
	"mtp_contract": {
	"base_hidden_variant": "post_norm",
	"concat_order": "embedding_hidden",
	"hidden_variant": "post_norm",
	"mtp_position_mode": "local",
	"mtp_quant_bits": 4,
	"mtp_quant_group_size": 64,
	"mtp_quant_mode": "affine"
	},
	"mtp_depth_max": 3,
	"mtp_sidecar": "native MTP sidecar",
	"mtplx_version": "1.0.4",
	"recommended_profile": "sustained",
	"sampler": {
	"temperature": 0.6,
	"top_k": 20,
	"top_p": 0.95
	},
	"speed_evidence": {
	"acceptance_by_depth": [
	0.90,
	0.78
	],
	"acceptance_collapsed": [],
	"depth": 2,
	"failure_reasons": [],
	"forge_verify_rows": [
	{
	"acceptance_by_position": [],
	"depth": 0,
	"finish_reasons": {"stop": 1},
	"hit_token_budget": false,
	"hit_token_budget_count": 0,
	"multiplier_vs_ar": 1.0,
	"quality_passed": true,
	"tok_s": 76.6,
	"verify_time_s": 5.0
	},
	{
	"acceptance_by_position": [0.90],
	"depth": 1,
	"finish_reasons": {"stop": 1},
	"hit_token_budget": false,
	"hit_token_budget_count": 0,
	"multiplier_vs_ar": 1.32,
	"quality_passed": true,
	"tok_s": 101.4,
	"verify_time_s": 5.0
	},
	{
	"acceptance_by_position": [0.90, 0.78],
	"depth": 2,
	"finish_reasons": {"stop": 1},
	"hit_token_budget": false,
	"hit_token_budget_count": 0,
	"multiplier_vs_ar": 1.33,
	"quality_passed": true,
	"tok_s": 101.8,
	"verify_time_s": 5.0
	}
	],
	"greedy_diagnostic": {
	"tok_s": 76.6
	},
	"quality_rejected": [],
	"tok_s": [101.8],
	"verdict": "mtp_depth_wins"
	},
	"verified_on": {
	"hardware": "macOS-15.7.3-arm64-arm-64bit",
	"machine_arch": "arm64",
	"macos": "15.7.3",
	"model": "Agents-A1-MTPLX",
	"timestamp": "2026-06-30T12:00:00+0800"
	}
	}