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.