README.md

---
license: mit
base_model:
- PRIME-RL/Eurus-2-7B-PRIME
- Qwen/Qwen2.5-7B-Instruct
tags:
- merge
- mergekit
- lazymergekit
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---

# Qwerus-7B

Qwerus-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [PRIME-RL/Eurus-2-7B-PRIME](https://huggingface.co/PRIME-RL/Eurus-2-7B-PRIME)
* [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)

Benchmark on reasoning tasks using lighteval:

|      Task       |Version|     Metric     |Value |   |Stderr|
|-----------------|------:|----------------|-----:|---|-----:|
|aime24  |      1|extractive_match|0.1333|±  |0.0631|
|math_500|      1|extractive_match|0.7420|±  |0.0196|

In comparison, Qwen2.5-7B-Instruct:

|      Task       |Version|     Metric     |Value |   |Stderr|
|-----------------|------:|----------------|-----:|---|-----:|
|aime24  |      1|extractive_match|0.1667|±  |0.0692|
|math_500|      1|extractive_match|0.8220|±  |0.0171|

## 🧩 Configuration

```yaml
models:
  - model: Qwen/Qwen2.5-7B
    # No parameters necessary for base model
  - model: PRIME-RL/Eurus-2-7B-PRIME
    parameters:
      density: 0.56
      weight: 0.5
  - model: Qwen/Qwen2.5-7B-Instruct
    parameters:
      density: 0.56
      weight: 0.5
merge_method: dare_ties
base_model: Qwen/Qwen2.5-7B
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Qwerus-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```