README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
- creative
- roleplay
- instruct
- qwen
- model_stock
- bfloat16
base_model:
- newsbang/Homer-v0.5-Qwen2.5-7B
- allknowingroger/HomerSlerp1-7B
- bunnycore/Qwen2.5-7B-Instruct-Fusion
- bunnycore/Qandora-2.5-7B-Creative
model-index:
- name: Qwen2.5-7B-HomerCreative-Mix
  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: 78.35
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
      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: 36.77
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
      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: 32.33
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
      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: 6.6
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
      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: 13.77
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
      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: 38.3
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
      name: Open LLM Leaderboard
---

# ZeroXClem/Qwen2.5-7B-HomerCreative-Mix

**ZeroXClem/Qwen2.5-7B-HomerCreative-Mix** is an advanced language model meticulously crafted by merging four pre-trained models using the powerful [mergekit](https://github.com/cg123/mergekit) framework. This fusion leverages the **Model Stock** merge method to combine the creative prowess of **Qandora**, the instructive capabilities of **Qwen-Instruct-Fusion**, the sophisticated blending of **HomerSlerp1**, and the foundational conversational strengths of **Homer-v0.5-Qwen2.5-7B**. The resulting model excels in creative text generation, contextual understanding, and dynamic conversational interactions.

## 🚀 Merged Models

This model merge incorporates the following:

- [**bunnycore/Qandora-2.5-7B-Creative**](https://huggingface.co/bunnycore/Qandora-2.5-7B-Creative): Specializes in creative text generation, enhancing the model's ability to produce imaginative and diverse content.

- [**bunnycore/Qwen2.5-7B-Instruct-Fusion**](https://huggingface.co/bunnycore/Qwen2.5-7B-Instruct-Fusion): Focuses on instruction-following capabilities, improving the model's performance in understanding and executing user commands.

- [**allknowingroger/HomerSlerp1-7B**](https://huggingface.co/allknowingroger/HomerSlerp1-7B): Utilizes spherical linear interpolation (SLERP) to blend model weights smoothly, ensuring a harmonious integration of different model attributes.

- [**newsbang/Homer-v0.5-Qwen2.5-7B**](https://huggingface.co/newsbang/Homer-v0.5-Qwen2.5-7B): Acts as the foundational conversational model, providing robust language comprehension and generation capabilities.

## 🧩 Merge Configuration

The configuration below outlines how the models are merged using the **Model Stock** method. This approach ensures a balanced and effective integration of the unique strengths from each source model.

```yaml
# Merge configuration for ZeroXClem/Qwen2.5-7B-HomerCreative-Mix using Model Stock

models:
  - model: bunnycore/Qandora-2.5-7B-Creative
  - model: bunnycore/Qwen2.5-7B-Instruct-Fusion
  - model: allknowingroger/HomerSlerp1-7B
merge_method: model_stock
base_model: newsbang/Homer-v0.5-Qwen2.5-7B
normalize: false
int8_mask: true
dtype: bfloat16
```

### Key Parameters

- **Merge Method (`merge_method`):** Utilizes the **Model Stock** method, as described in [Model Stock](https://arxiv.org/abs/2403.19522), to effectively combine multiple models by leveraging their strengths.
  
- **Models (`models`):** Specifies the list of models to be merged:
  - **bunnycore/Qandora-2.5-7B-Creative:** Enhances creative text generation.
  - **bunnycore/Qwen2.5-7B-Instruct-Fusion:** Improves instruction-following capabilities.
  - **allknowingroger/HomerSlerp1-7B:** Facilitates smooth blending of model weights using SLERP.
  
- **Base Model (`base_model`):** Defines the foundational model for the merge, which is **newsbang/Homer-v0.5-Qwen2.5-7B** in this case.
  
- **Normalization (`normalize`):** Set to `false` to retain the original scaling of the model weights during the merge.
  
- **INT8 Mask (`int8_mask`):** Enabled (`true`) to apply INT8 quantization masking, optimizing the model for efficient inference without significant loss in precision.
  
- **Data Type (`dtype`):** Uses `bfloat16` to maintain computational efficiency while ensuring high precision.

## 🏆 Performance Highlights

- **Creative Text Generation:** Enhanced ability to produce imaginative and diverse content suitable for creative writing, storytelling, and content creation.
  
- **Instruction Following:** Improved performance in understanding and executing user instructions, making the model more responsive and accurate in task execution.
  
- **Optimized Inference:** INT8 masking and `bfloat16` data type contribute to efficient computation, enabling faster response times without compromising quality.

## 🎯 Use Case & Applications

**ZeroXClem/Qwen2.5-7B-HomerCreative-Mix** is designed to excel in environments that demand both creative generation and precise instruction following. Ideal applications include:

- **Creative Writing Assistance:** Aiding authors and content creators in generating imaginative narratives, dialogues, and descriptive text.
  
- **Interactive Storytelling and Role-Playing:** Enhancing dynamic and engaging interactions in role-playing games and interactive storytelling platforms.
  
- **Educational Tools and Tutoring Systems:** Providing detailed explanations, answering questions, and assisting in educational content creation with contextual understanding.
  
- **Technical Support and Customer Service:** Offering accurate and contextually relevant responses in technical support scenarios, improving user satisfaction.
  
- **Content Generation for Marketing:** Creating compelling and diverse marketing copy, social media posts, and promotional material with creative flair.

## 📝 Usage

To utilize **ZeroXClem/Qwen2.5-7B-HomerCreative-Mix**, follow the steps below:

### Installation

First, install the necessary libraries:

```bash
pip install -qU transformers accelerate
```

### Example Code

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/Qwen2.5-7B-HomerCreative-Mix"

# 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 = "Once upon a time in a land far, far away,"

# 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"])
```

### Notes

- **Fine-Tuning:** This merged model may require fine-tuning to optimize performance for specific applications or domains.
  
- **Resource Requirements:** Ensure that your environment has sufficient computational resources, especially GPU-enabled hardware, to handle the model efficiently during inference.
  
- **Customization:** Users can adjust parameters such as `temperature`, `top_k`, and `top_p` to control the creativity and diversity of the generated text.


## 📜 License

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

## 💡 Tags

- `merge`
- `mergekit`
- `model_stock`
- `Qwen`
- `Homer`
- `Creative`
- `ZeroXClem/Qwen2.5-7B-HomerCreative-Mix`
- `bunnycore/Qandora-2.5-7B-Creative`
- `bunnycore/Qwen2.5-7B-Instruct-Fusion`
- `allknowingroger/HomerSlerp1-7B`
- `newsbang/Homer-v0.5-Qwen2.5-7B`

---
# [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__Qwen2.5-7B-HomerCreative-Mix)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |34.35|
|IFEval (0-Shot)    |78.35|
|BBH (3-Shot)       |36.77|
|MATH Lvl 5 (4-Shot)|32.33|
|GPQA (0-shot)      | 6.60|
|MuSR (0-shot)      |13.77|
|MMLU-PRO (5-shot)  |38.30|