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README.md
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- merge
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- mergekit
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- lazymergekit
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---
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# ZeroXClem-Llama-3.1-8B-AthenaSky-MegaMix
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name: ZeroXClem-Llama-3.1-8B-AthenaSky-MegaMix
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base_model: mergekit-community/L3.1-Athena-d-8B
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dtype: bfloat16
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- model: Undi95/Meta-Llama-3.1-8B-Claude
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- model: mergekit-community/good_mix_model_Stock
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tokenizer_source: mergekit-community/L3.1-Athena-d-8B
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```
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- merge
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- mergekit
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- lazymergekit
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- model_stock
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- ZeroXClem-Llama-3.1-8B-AthenaSky-MegaMix
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language:
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- en
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base_model:
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- Pedro13543/mega_blend_model
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- Skywork/Skywork-o1-Open-Llama-3.1-8B
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- Undi95/Meta-Llama-3.1-8B-Claude
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- mergekit-community/good_mix_model_Stock
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- mergekit-community/L3.1-Athena-d-8B
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pipeline_tag: text-generation
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library_name: transformers
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---
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# ZeroXClem-Llama-3.1-8B-AthenaSky-MegaMix
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## Overview
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**ZeroXClem-Llama-3.1-8B-AthenaSky-MegaMix** is a powerful AI model built through **model stock merging** using **MergeKit**. It brings together some of the best models available on **Hugging Face**, ensuring strong performance in a wide range of NLP tasks, including reasoning, coding, roleplay, and instruction-following.
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This model was created by merging high-quality foundational and fine-tuned models to create an optimized **blended architecture** that retains the strengths of each contributing model.
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## Merge Details
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- **Merge Method:** `model_stock`
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- **Base Model:** [`mergekit-community/L3.1-Athena-d-8B`](https://huggingface.co/mergekit-community/L3.1-Athena-d-8B)
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- **Dtype:** `bfloat16`
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- **Tokenizer Source:** `mergekit-community/L3.1-Athena-d-8B`
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## Models Merged
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The following models contributed to this fusion:
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- [`Pedro13543/mega_blend_model`](https://huggingface.co/Pedro13543/mega_blend_model) - A well-balanced blend of roleplay and instruction-tuned Llama-3.1 variants.
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- [`Skywork/Skywork-o1-Open-Llama-3.1-8B`](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B) - Optimized for reasoning and slow-thinking capabilities.
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- [`Undi95/Meta-Llama-3.1-8B-Claude`](https://huggingface.co/Undi95/Meta-Llama-3.1-8B-Claude) - Fine-tuned on Claude Opus/Sonnet data, improving response depth and conversational engagement.
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- [`mergekit-community/good_mix_model_Stock`](https://huggingface.co/mergekit-community/good_mix_model_Stock) - A diverse mixture including RP-focused and knowledge-heavy datasets.
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## Configuration
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```yaml
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name: ZeroXClem-Llama-3.1-8B-AthenaSky-MegaMix
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base_model: mergekit-community/L3.1-Athena-d-8B
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dtype: bfloat16
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- model: Undi95/Meta-Llama-3.1-8B-Claude
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- model: mergekit-community/good_mix_model_Stock
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tokenizer_source: mergekit-community/L3.1-Athena-d-8B
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```
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## Features & Improvements
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๐น **Advanced Reasoning & Thoughtfulness** - Thanks to `Skywork-o1` integration, this model excels in logical thinking and problem-solving.
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๐น **Enhanced Conversational Depth** - The inclusion of `Meta-Llama-3.1-8B-Claude` adds better response structuring, making it more engaging in dialogue.
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๐น **Versatile Roleplay & Creativity** - Leveraging `mega_blend_model` and `good_mix_model_Stock`, the model supports immersive roleplaying and storytelling.
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๐น **Strong Instruction Following** - Trained on various instruction datasets to provide clear, informative, and helpful responses.
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## Use Cases
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- **Chat & Roleplay** - Supports natural, engaging, and dynamic conversational flow.
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- **Programming & Code Generation** - Provides reliable code completions and debugging suggestions.
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- **Creative Writing** - Generates compelling stories, character dialogues, and immersive text.
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- **Educational Assistance** - Helps explain complex topics and answer academic questions.
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- **Logic & Problem-Solving** - Can handle reasoning-based and structured thought processes.
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## ๐ How to Use
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### ๐ฅ Ollama (Quick Inference)
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You can run the model using **Ollama** for direct testing:
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```bash
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ollama run hf.co/ZeroXClem/Llama-3.1-8B-AthenaSky-MegaMix
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```
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### ๐ค Hugging Face Transformers (Python)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import torch
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model_name = "ZeroXClem/Llama-3.1-8B-AthenaSky-MegaMix"
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# Load tokenizer & model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Initialize text generation pipeline
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text_generator = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Example prompt
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prompt = "Describe the significance of AI ethics in modern technology."
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# Generate output
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outputs = text_generator(
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prompt,
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max_new_tokens=200,
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do_sample=True,
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temperature=0.7,
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top_k=50,
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top_p=0.95
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)
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print(outputs[0]["generated_text"])
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```
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---
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## Model Alignment & Ethics
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โ ๏ธ **Uncensored Use**: This model does not apply strict moderation. Users should implement appropriate **safety filters** before deployment.
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โ ๏ธ **Responsibility Notice**: You are responsible for the outputs generated by this model. It is recommended to apply **ethical safeguards** and **content moderation** when integrating this model into applications.
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๐ **License**: Governed by the **Meta Llama 3.1 Community License Agreement**.
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## Feedback & Contributions
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We welcome feedback, bug reports, and performance evaluations! If you find improvements or wish to contribute, feel free to reach out or submit suggestions.
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---
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**ZeroXClem Team | 2025**
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