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---
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"])
``` |