---
language:
- en
- zh
license: apache-2.0
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
- Locutusque/StockQwen-2.5-7B
- allknowingroger/QwenSlerp8-7B
base_model:
- allknowingroger/QwenSlerp8-7B
- Locutusque/StockQwen-2.5-7B
model-index:
- name: Qwen-2.5-Aether-SlerpFusion-7B
  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: 62.62
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
      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: 36.01
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
      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: 24.17
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
      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: 6.49
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
      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: 11.29
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
      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: 36.96
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B
      name: Open LLM Leaderboard
---

# ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B

**Qwen-2.5-Aether-SlerpFusion-7B** is a sophisticated model merge that combines the strengths of multiple pre-trained language models using the powerful [mergekit](https://github.com/ZeroXClem/mergekit) framework. This fusion leverages spherical linear interpolation (SLERP) to seamlessly blend architectural layers, resulting in a model that benefits from enhanced performance and versatility.

## 🚀 Merged Models

This model merge incorporates the following:

- [**Locutusque/StockQwen-2.5-7B**](https://huggingface.co/Locutusque/StockQwen-2.5-7B): Serves as the foundational model, renowned for its robust language understanding and generation capabilities.
- [**allknowingroger/QwenSlerp8-7B**](https://huggingface.co/allknowingroger/QwenSlerp8-7B): Contributes advanced task-specific fine-tuning, enhancing the model's adaptability across various applications.

## 🧩 Merge Configuration

The configuration below outlines how the models are merged using **spherical linear interpolation (SLERP)**. This method ensures smooth transitions between the layers of both models, facilitating an optimal blend of their unique attributes:

```yaml
# ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B Merge Configuration
slices:
  - sources:
      - model: Locutusque/StockQwen-2.5-7B
        layer_range: [0, 28]
      - model: allknowingroger/QwenSlerp8-7B
        layer_range: [0, 28]
merge_method: slerp
base_model: Locutusque/StockQwen-2.5-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```

### 🔑 Key Parameters

- **Self-Attention Filtering** (`self_attn`): Controls the blending extent across self-attention layers, allowing for a dynamic mix between the two source models.
- **MLP Filtering** (`mlp`): Adjusts the balance within the Multi-Layer Perceptrons, fine-tuning the model’s neural network layers for optimal performance.
- **Global Weight (`t.value`)**: Sets a general interpolation factor for all unspecified layers, ensuring an equal contribution from both models.
- **Data Type (`dtype`)**: Utilizes `bfloat16` to maintain computational efficiency while preserving high precision.

### 🗣️ Inference

Below is an example of how to load and use the model for text generation:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

# Define the model name
model_name = "ZeroXClem/Qwen-2.5-Aether-SlerpFusion-7B"

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Initialize the pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Define the input prompt
prompt = "Explain the significance of artificial intelligence in modern healthcare."

# Generate the output
outputs = text_generator(
    prompt,
    max_new_tokens=150,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

# Print the generated text
print(outputs[0]["generated_text"])
```

## 🎯 Use Case & Applications

**Qwen-2.5-Aether-SlerpFusion-7B** excels in scenarios that require both robust language understanding and specialized task performance. This merged model is ideal for:

- **Advanced Text Generation and Comprehension**: Crafting coherent, contextually accurate, and nuanced text for applications like content creation, summarization, and translation.
- **Domain-Specific Tasks**: Enhancing performance in specialized areas such as legal document analysis, medical information processing, and technical support.
- **Interactive AI Systems**: Powering conversational agents and chatbots that require both general language capabilities and task-specific expertise.

## 📜 License

This model is open-sourced under the **Apache-2.0 License**.

## 💡 Tags

- `merge`
- `mergekit`
- `slerp`
- `Qwen`
- `Locutusque/StockQwen-2.5-7B`
- `allknowingroger/QwenSlerp8-7B`

---
# [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_ZeroXClem__Qwen-2.5-Aether-SlerpFusion-7B)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |29.59|
|IFEval (0-Shot)    |62.62|
|BBH (3-Shot)       |36.01|
|MATH Lvl 5 (4-Shot)|24.17|
|GPQA (0-shot)      | 6.49|
|MuSR (0-shot)      |11.29|
|MMLU-PRO (5-shot)  |36.96|