QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF

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: numfewshot: 0 metrics:
• —type: instlevelstrictacc and promptlevelstrictacc value: 77.08 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?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: numfewshot: 3 metrics:
• —type: accnorm value: 36.58 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllm_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/competitionmath args: numfew_shot: 4 metrics:
• —type: exactmatch value: 29.53 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllm_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: numfewshot: 0 metrics:
• —type: accnorm value: 9.28 name: accnorm source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?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: numfewshot: 0 metrics:
• —type: accnorm value: 14.41 name: accnorm source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?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: numfewshot: 5 metrics:
• —type: acc value: 38.13 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?query=ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix name: Open LLM Leaderboard


QuantFactory/Qwen2.5-7B-HomerAnvita-NerdMix-GGUF

This is quantized version of 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 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.

💡 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

Detailed results can be found here