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

Overview

ZeroXClem-Llama-3.1-8B-Athena-Apollo-exp is a powerful AI model built through Model Stock merging using MergeKit. It merges several of the most capable and nuanced Llama-3.1-based models available on Hugging Face, optimized for performance across instruction-following, roleplay, logic, coding, and creative writing tasks.

By fusing diverse fine-tuned architectures into a cohesive blended model, this creation delivers excellent generalist abilities while retaining specialized strengths.


🔧 Merge Details


💡 Models Merged

The following models contribute to this powerful fusion:


🧪 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

✨ Features & Highlights

🔹 Instruction-Following Prowess — Merged from Tulu-aligned and instruct-tuned models like Apollo-exp and Athena-k for high-quality, context-aware responses.

🔹 Immersive Roleplay & Personality — Strong roleplay personas and emotional nuance thanks to Athena's diverse RP blends.

🔹 Creative & Structured Generation — Support for creative writing, long-context novelization, and formal logic modeling from l2/l3 integrations.

🔹 Depth in Dialogue — Enhanced ability to carry layered and philosophical conversation from Claude-style fine-tunes in Apollo-exp.


🎯 Use Cases

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

🛠️ Usage Instructions

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 Instructions

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

🧭 Alignment & Ethics

⚠️ 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.

📜 License: Usage governed by the Meta Llama 3.1 Community License.


💌 Feedback & Contributions

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

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