Text Generation
MLX
Safetensors
English
qwen3_5_moe
quantized
4bit
mtp
qwen3
agents
conversational
4-bit precision
Instructions to use wang-yang/Agents-A1-MTPLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- Pi
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"} ] }'
File size: 2,923 Bytes
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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"
}
}
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