---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- ptd
- sparse-transformer
- qwen2
- long-context
- custom-code
base_model:
- Qwen/Qwen2.5-0.5B
---

# PTD Qwen2.5-0.5B Keep70 Variant

PTD (Physical Token Dropping) keep-rate 70% variant of Qwen2.5-0.5B.

- Base model: `Qwen/Qwen2.5-0.5B`
- Variant: `PTD keep70` (full-state)
- Recommended keep-rate: `0.7`
- Runtime: custom HF remote code (`trust_remote_code=True`)

## Repository Links

- GitHub project: https://github.com/mhndayesh/Physical-Token-Dropping-PTD
- PTD engineering docs: https://github.com/mhndayesh/Physical-Token-Dropping-PTD/tree/main/FINAL_ENG_DOCS

## What Is Included

- `ptd_model_state.pt`: full PTD model weights (base + PTD components)
- `config.json`: HF auto-map for custom loading
- `configuration_ptd_qwen2.py`, `modeling_ptd_qwen2.py`: HF custom classes
- `model.py`: PTD runtime implementation
- `ptd_package_config.json`: package metadata and PTD config

## Quick Start (Transformers)

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "mhndayesh/PTD-Qwen2.5-0.5B-Keep70-Variant"

model = AutoModelForCausalLM.from_pretrained(
    repo,
    trust_remote_code=True,
    dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
inputs = tokenizer("PTD cache test:", return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=32, do_sample=False, use_cache=True)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```

## Notes

- This model uses PTD sparse routing and custom runtime behavior.
- For reproducibility commands and benchmarks, see the GitHub docs linked above.