README.md

---

language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
- bfloat16
- roleplay
- creative
- instruct
- anvita
- qwen
- nerd
- homer
- Qandora
base_model:
- bunnycore/Qandora-2.5-7B-Creative
- allknowingroger/HomerSlerp1-7B
- sethuiyer/Qwen2.5-7B-Anvita
- fblgit/cybertron-v4-qw7B-MGS
- jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
- newsbang/Homer-v0.5-Qwen2.5-7B
pipeline_tag: text-generation
model-index:
- name: Qwen2.5-7B-HomerAnvita-NerdMix
  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: 77.08
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
      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: 36.58
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
      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: 29.53
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
      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: 9.28
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
      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: 14.41
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
      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: 38.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
      name: Open LLM Leaderboard

---

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# QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF
This is quantized version of [ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix](https://huggingface.co/ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix) created using llama.cpp

# Original Model Card


# ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix

**ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix** is an advanced language model meticulously crafted by merging five 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**, the mathematical precision of **Cybertron-MGS**, and the uncensored expertise of **Qwen-Nerd**. The resulting model excels in creative text generation, contextual understanding, technical reasoning, 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.

- [**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.

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

- [**fblgit/cybertron-v4-qw7B-MGS**](https://huggingface.co/fblgit/cybertron-v4-qw7B-MGS): Enhances mathematical reasoning and precision, enabling the model to handle complex computational tasks effectively.

- [**jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0**](https://huggingface.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0): Provides uncensored expertise and robust technical knowledge, making the model suitable for specialized technical support and information retrieval.

- [**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-HomerAnvita-NerdMix using Model Stock

models:
  - model: bunnycore/Qandora-2.5-7B-Creative
  - model: allknowingroger/HomerSlerp1-7B
  - model: sethuiyer/Qwen2.5-7B-Anvita
  - model: fblgit/cybertron-v4-qw7B-MGS
  - model: jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
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.
  - **allknowingroger/HomerSlerp1-7B:** Facilitates smooth blending of model weights using SLERP.
  - **sethuiyer/Qwen2.5-7B-Anvita:** Improves instruction-following capabilities.
  - **fblgit/cybertron-v4-qw7B-MGS:** Enhances mathematical reasoning and precision.
  - **jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0:** Provides uncensored technical expertise.

- **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.
  
- **Mathematical Reasoning:** Enhanced capability to handle complex computational tasks with high precision, suitable for technical and analytical applications.
  
- **Uncensored Technical Expertise:** Provides robust technical knowledge without content restrictions, making it ideal for specialized technical support and information retrieval.
  
  
- **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-HomerAnvita-NerdMix** is designed to excel in environments that demand a combination of creative generation, precise instruction following, mathematical reasoning, and technical expertise. 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.
  
- **Mathematical Problem Solving:** Assisting in solving complex mathematical problems and providing step-by-step explanations for educational purposes.
  
- **Technical Documentation and Analysis:** Generating detailed technical documents, reports, and analyses with high precision and clarity.

## 📝 Usage

To utilize **ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix**, 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-HomerAnvita-NerdMix"

# 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"])
```

### 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`
- `Anvita`
- `Nerd`
- `ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix`
- `bunnycore/Qandora-2.5-7B-Creative`
- `allknowingroger/HomerSlerp1-7B`
- `sethuiyer/Qwen2.5-7B-Anvita`
- `fblgit/cybertron-v4-qw7B-MGS`
- `jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0`
- `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-HomerAnvita-NerdMix)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |34.17|
|IFEval (0-Shot)    |77.08|
|BBH (3-Shot)       |36.58|
|MATH Lvl 5 (4-Shot)|29.53|
|GPQA (0-shot)      | 9.28|
|MuSR (0-shot)      |14.41|
|MMLU-PRO (5-shot)  |38.13|