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---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
base_model:
- Qwen/Qwen2.5-72B-Instruct
---
# BigQwen2.5-125B-Instruct
BigQwen2.5-125B-Instruct is a [Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) self-merge made with [MergeKit](https://github.com/arcee-ai/mergekit/tree/main).
It applies the [mlabonne/Meta-Llama-3-120B-Instruct](https://huggingface.co/mlabonne/Meta-Llama-3-120B-Instruct/) recipe.
I made it due to popular demand but I haven't tested it so use it at your own risk. ¯\\\_(ツ)_/¯
## 🔍 Applications
It might be good for creative writing tasks. I recommend a context length of 32k but you can go up to 131,072 tokens in theory.
## 🏆 Evaluation
I think it's too big for the Open LLM Leaderboard, unfortunately. Here's some feedback from people who tried it (thanks a lot!):
## 🧩 Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- layer_range: [0, 20]
model: Qwen/Qwen2.5-72B-Instruct
- sources:
- layer_range: [10, 30]
model: Qwen/Qwen2.5-72B-Instruct
- sources:
- layer_range: [20, 40]
model: Qwen/Qwen2.5-72B-Instruct
- sources:
- layer_range: [30, 50]
model: Qwen/Qwen2.5-72B-Instruct
- sources:
- layer_range: [40, 60]
model: Qwen/Qwen2.5-72B-Instruct
- sources:
- layer_range: [50, 70]
model: Qwen/Qwen2.5-72B-Instruct
- sources:
- layer_range: [60, 80]
model: Qwen/Qwen2.5-72B-Instruct
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/BigQwen2.5-125B-Instruct"
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"])
``` |