Instructions to use ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp")
model = AutoModelForCausalLM.from_pretrained("ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp

SGLang

How to use ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp with Docker Model Runner:
```
docker model run hf.co/ZeroXClem/Llama-3.1-8B-Athena-Apollo-exp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 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

Merge Method: model_stock
Base Model: mergekit-community/L3.1-Athena-l3-8B
Dtype: bfloat16
Tokenizer Source: mergekit-community/L3.1-Athena-l3-8B

💡 Models Merged

The following models contribute to this powerful fusion:

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

✨ 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.