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 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: Specializes in creative text generation, enhancing the model's ability to produce imaginative and diverse content.
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: Focuses on instruction-following capabilities, improving the model's performance in understanding and executing user commands.
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: 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: 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.

# 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, 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:

pip install -qU transformers accelerate

Example Code

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

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.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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

ZeroXClem
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Qwen2.5-7B-HomerAnvita-NerdMix