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wang-yang/Agents-A1-MTPLX-Q4

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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Agents-A1-MTPLX-Q4

4-bit quantized MLX version of InternScience/Agents-A1 with grafted MTP (Multi-Token Prediction) head for speculative decoding on Apple Silicon.

Model Details

  • Base model: InternScience/Agents-A1 (Qwen3.5-MoE architecture, 35B total / 3B active parameters)
  • Quantization: 4-bit affine (group size 64), router gates at 8-bit
  • MTP head: Grafted from Qwen3.5-35B-A3B (4-bit quantized, 1 layer)
  • Format: MLX safetensors
  • Disk size: ~18 GB (model) + 1.6 GB (MTP sidecar)

Architecture

  • Hidden size: 2048
  • Layers: 40 (hybrid linear + full attention)
  • Experts: 256 total, 8 active per token
  • Vocab: 248,320
  • Context: 262,144 tokens

Usage with MTPLX

bash
mtplx start --model wang-yang/Agents-A1-MTPLX-Q4

Usage with mlx-lm

python
from mlx_lm import load, generate

model, tokenizer = load("wang-yang/Agents-A1-MTPLX-Q4")
prompt = "<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"
result = generate(model, tokenizer, prompt=prompt, max_tokens=200)

Notes

  • EOS token: <|im_end|> (id 248046)
  • MTP speculative decoding: ~1.33x speedup (D2 best, 101.8 tok/s vs AR 76.6 tok/s on M3 Max 128GB).

Files

FileDescription
model-0000X-of-00004.safetensorsQuantized model weights (4 shards)
mtp.safetensorsMTP draft head weights (4-bit quantized)
config.jsonModel architecture + quantization config
tokenizer.jsonTokenizer vocabulary
tokenizer_config.jsonTokenizer settings
chat_template.jinjaChat template (no thinking mode)