---
language:
- en
license: other
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
base_model:
- Qwen/Qwen2.5-32B-Instruct
license_name: tongyi-qianwen
license_link: https://huggingface.co./Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: BigQwen2.5-52B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 79.29
      name: strict accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 59.81
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 17.82
      name: exact match
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.94
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 10.45
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 50.22
      name: accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-52B-Instruct
      name: Open LLM Leaderboard
---
# BigQwen2.5-52B-Instruct

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/98GiKtmH1AtHHbIbOUH4Y.jpeg)

BigQwen2.5-52B-Instruct is a [Qwen/Qwen2-32B-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.

## 🧩 Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
- sources:
  - layer_range: [0, 16]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [8, 24]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [16, 32]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [24, 40]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [32, 48]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [40, 56]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [56, 64]
    model: Qwen/Qwen2.5-32B-Instruct
merge_method: passthrough
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/BigQwen2.5-52B-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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_mlabonne__BigQwen2.5-52B-Instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |37.42|
|IFEval (0-Shot)    |79.29|
|BBH (3-Shot)       |59.81|
|MATH Lvl 5 (4-Shot)|17.82|
|GPQA (0-shot)      | 6.94|
|MuSR (0-shot)      |10.45|
|MMLU-PRO (5-shot)  |50.22|