---
license: apache-2.0
---

# QwQ-1.5B-Persona

## Introduction

QwQ-1.5B-Persona is finetuned from Qwen2.5-1.5B-Instruct on 1 million math persona data (see [this paper](https://arxiv.org/abs/2406.20094) for details about how to construct the data).

Currently QwQ-1.5B-Persona is meant to serve as a draft model for losslessly accelerating the inference of QwQ-32B, but you may also use it as a standalone model.

## Evaluation

We provide the speedup of QwQ 32B on MATH (200 samples spanning level 1-5), GPQA (diamond), and AIME (2023,2024). All experiments are run on 2 A100 with 40G memory each.

**Qwen2.5-1.5B-Instruct** as draft model:
| Draft Length Policy | MATH (l1) | MATH (l2) | MATH (l3) | MATH (l4) | MATH (l5) | GPQA | AIME | Avg |
| ------------------- | --------- | --------- | --------- | --------- | --------- | ---- | ---- | ---- |
| **Constant** | 1.18 | 1.16 | 1.22 | 1.29 | 1.30 | 1.28 | 1.13 | 1.22 |
| **Heuristics** | 1.12 | 1.15 | 1.17 | 1.19 | 1.22 | 1.25 | 1.13 | 1.18 |
| **SVIP** | 1.45 | 1.47 | 1.51 | 1.58 | 1.61 | 1.57 | 1.45 | 1.52 |

**QwQ-1.5B-Persona** as draft model:
| Draft Length Policy | MATH (l1) | MATH (l2) | MATH (l3) | MATH (l4) | MATH (l5) | GPQA | AIME | Avg |
| ------------------- | --------- | --------- | --------- | --------- | --------- | ---- | ---- | ---- |
| **Constant** | 1.45 | 1.50 | 1.52 | 1.56 | 1.56 | 1.58 | 1.25 | 1.49 |
| **Heuristics** | 1.29 | 1.26 | 1.27 | 1.30 | 1.33 | 1.34 | 1.18 | 1.28 |
| **SVIP** | 1.65 | 1.68 | 1.75 | 1.78 | 1.82 | 1.77 | 1.52 | 1.71 |

The three draft length policies are:

- **Constant**: A constant draft length of 5
- **Heuristics**: If all draft tokens are accepted in one round, draft length is increased by 2 in the next round; otherwise it's decreased by 1. This policy is implemented in the [transformers library](https://github.com/huggingface/transformers/blob/0531d7513b617f7c5f8b5f333985c63f0edd5fe2/src/transformers/generation/utils.py#L1906).
- **SVIP**: a dynamic draft length policy that adaptively determines when to stop drafting based on draft model entropy. See [this paper](https://arxiv.org/abs/2411.18462) for details.

## Quickstart

The constant and heuristics draft length policies have been integrated into transformers library. Here is a code snippet for using QwQ-1.5B-Persona to accelerate the inference of QwQ 32B:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/QwQ-32B-Preview",
    torch_dtype="auto",
    device_map={'': 0}
)

draft_model = AutoModelForCausalLM.from_pretrained(
    "Geralt-Targaryen/QwQ-1.5B-Persona",
    torch_dtype="auto",
    device_map={'': 0}
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/QwQ-32B-Preview")

prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
    assistant_model=draft_model
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

For the more advanced SVIP draft length policy, please refer to [this GitHub repo](https://github.com/Geralt-Targaryen/SVIP).

## Citation

If you find QwQ-1.5B-Persona to be helpful, please cite the following paper.

```
@misc{zhang2024svip,
      title={Draft Model Knows When to Stop: A Self-Verification Length Policy for Speculative Decoding},
      author={Ziyin Zhang and Jiahao Xu and Tian Liang and Xingyu Chen and Zhiwei He and Rui Wang and Zhaopeng Tu},
      year={2024},
      eprint={2411.18462},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.18462},
}
```