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license: other
license_name: modilify-open-model-license-1.0
license_link: LICENSE
library_name: transformers
pipeline_tag: text-generation
tags:
- diffusion
- mixture-of-experts
- trust-remote-code
- safetensors
---
# 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.
```python
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.
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