Llama 3.1 8B Athena Apollo Exp

🚀 ZeroXClem-Llama-3.1-8B-Athena-Apollo-exp

ZeroXClem-Llama-3.1-8B-Athena-Apollo-exp is a powerful AI model. It is created through Model Stock merging using MergeKit. This model combines several top - notch Llama - 3.1 - based models available on Hugging Face. It is optimized for various tasks, including instruction - following, roleplay, logic, coding, and creative writing. By integrating different fine - tuned architectures, it offers excellent general capabilities while maintaining specialized strengths.

🚀 Quick Start

🐍 Python Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Explain quantum entanglement like I'm 10 years old."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🐪 Ollama Usage

ollama run hf.co/ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp-Q4_K_M-GGUF

✨ Features

• Instruction - Following Prowess: Merged from Tulu - aligned and instruct - tuned models like Apollo - exp and Athena - k, it can provide high - quality, context - aware responses.
• Immersive Roleplay & Personality: Thanks to Athena's diverse RP blends, it has strong roleplay personas and emotional nuance.
• Creative & Structured Generation: It supports creative writing, long - context novelization, and formal logic modeling from l2/l3 integrations.
• Depth in Dialogue: Enhanced ability to carry layered and philosophical conversations from Claude - style fine - tunes in Apollo - exp.

📦 Installation

No specific installation steps are provided in the original README. If you want to use this model, you can follow the usage examples above.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Explain quantum entanglement like I'm 10 years old."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advanced Usage

There is no advanced usage example in the original README. You can adjust parameters such as max_new_tokens according to your specific needs.

📚 Documentation

🔧 Merge Details

💡 Models Merged

• rootxhacker/Apollo-exp-8B: A rich blend focused on alignment, DPO, and SFT instruction tuning across Llama - 3.1 variants.
• mergekit-community/L3.1-Athena-k-8B: Roleplay and safety - aligned merge based on Meta's Llama - 3.1 foundation.
• mergekit-community/L3.1-Athena-l2-8B: LoRA - enhanced with long - context and creative capability merges.
• mergekit-community/L3.1-Athena-l-8B: Deeply infused with LoRA - based domain - specific models in logic, psychology, storytelling, and more.

🧪 Configuration

name: ZeroXClem-Llama-3.1-8B-Athena-Apollo-exp
base_model: mergekit-community/L3.1-Athena-l3-8B
dtype: bfloat16
merge_method: model_stock
models:
  - model: rootxhacker/Apollo-exp-8B
  - model: mergekit-community/L3.1-Athena-k-8B
  - model: mergekit-community/L3.1-Athena-l2-8B
  - model: mergekit-community/L3.1-Athena-l-8B
tokenizer_source: mergekit-community/L3.1-Athena-l3-8B

🎯 Use Cases

• Conversational AI & Roleplay Bots
• Formal Reasoning & Chain - of - Thought Tasks
• Creative Writing & Storytelling Tools
• Coding Assistants
• Educational and Research Applications

📄 License

Usage is governed by the Meta Llama 3.1 Community License.

⚠️ Important Note

• Unfiltered Output: This model is uncensored and may generate content outside of alignment norms. Please implement your own moderation layers when deploying in production environments.
• Responsible Use: Developers are encouraged to audit outputs and maintain ethical usage policies for downstream applications.

💡 Usage Tip

We welcome your feedback, benchmarks, and improvements! Please open an issue or PR to contribute or tag us in your results and projects.

ZeroXClem Team | 2025