modilify/Modilify-Mk2-preview

Modilify Mk2 Preview

PyTorch text inference for the step 1250, schema25 checkpoint migrated from Modilify-Mk2-preview-mlx. The model uses a shared DiffusionGemma text encoder/decoder, a rolling 256-token canvas, and dual-timescale GDN2 memory. The release contains 32 Safetensors shards totaling 48.23 GiB.

Dense and expert LoRA adapters remain unfused. Model loading preserves the 26 FP32 GDN2/norm parameters alongside BF16 weights. No vision tower is included.

Inference

Use Transformers 5.14.1, PyTorch, Accelerate, and Safetensors. Run from this directory or replace model_path with its location. Inference needs additional memory beyond the weights.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "."
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
).eval()
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain why the sky is blue."}],
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_dict=True,
    return_tensors="pt",
).to(model.device)
output = model.generate(**inputs, max_new_tokens=128, seed=42)
print(tokenizer.decode(output.sequences[0, inputs.input_ids.shape[1]:],
                       skip_special_tokens=True))

For MPS, use device_map={"": "mps"}. Set enable_thinking=True in the chat template to request thinking. Generation uses temperature 0.8, top-k 40, min-p 0.05, target confidence 0.5, and failure budget 0.2. max_denoising_steps can bound generation. Static batches and continuous batching share the confidence-prefix commit policy; each request retains its own cache and RNG.