Instructions to use ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp with Transformers:

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

pipe = pipeline("text-generation", model="ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp")
model = AutoModelForCausalLM.from_pretrained("ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp")
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/Qwen2.5-7B-HomerFuse-NerdExp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"
# 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/Qwen2.5-7B-HomerFuse-NerdExp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp

SGLang

How to use ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp 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/Qwen2.5-7B-HomerFuse-NerdExp" \
    --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/Qwen2.5-7B-HomerFuse-NerdExp",
		"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/Qwen2.5-7B-HomerFuse-NerdExp" \
        --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/Qwen2.5-7B-HomerFuse-NerdExp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp with Docker Model Runner:
```
docker model run hf.co/ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ZeroXClem-Qwen2.5-7B-HomerFuse-NerdExp

🚀 Overview

ZeroXClem-Qwen2.5-7B-HomerFuse-NerdExp is a powerful and finely-tuned AI model built on HomerSlerp6-7B, with a fusion of Qwen2.5-7B-based models to create a unique blend of reasoning, creativity, and enhanced conversational depth. This model is an experimental fusion designed to bring high adaptability, deep knowledge, and engaging responses across a wide variety of use cases.

🛠 Merge Details

Merge Method: model_stock
Base Model: allknowingroger/HomerSlerp6-7B
Data Type: bfloat16
Tokenizer Source: allknowingroger/HomerSlerp6-7B

🔗 Merged Models

This fusion includes carefully selected models to enhance general intelligence, technical depth, and roleplay capabilities:

Model Name	Description
jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0	A knowledge-rich, uncensored model with deep expertise in multiple domains.
bunnycore/Blabbertron-1.0	A model optimized for free-flowing and expressive conversation.
bunnycore/Qwen2.5-7B-Fuse-Exp	Experimental fusion of Qwen2.5-based models for nuanced understanding.
Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview	Enhanced context comprehension and complex reasoning capabilities.

⚙ Configuration

name: ZeroXClem-Qwen2.5-7B-HomerFuse-NerdExp
base_model: allknowingroger/HomerSlerp6-7B
dtype: bfloat16
merge_method: model_stock
models:
  - model: jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0
  - model: bunnycore/Blabbertron-1.0
  - model: bunnycore/Qwen2.5-7B-Fuse-Exp
  - model: Xiaojian9992024/Qwen2.5-Dyanka-7B-Preview
tokenizer_source: allknowingroger/HomerSlerp6-7B

🧠 Why This Model?

✅ Balanced Fusion – A well-calibrated mix of reasoning, factual accuracy, and expressive depth.
✅ Uncensored Knowledge – Suitable for academic, technical, and exploratory conversations.
✅ Enhanced Context Retention – Ideal for long-form discussions and in-depth analysis.
✅ Diverse Applications – Can handle creative writing, roleplay, and problem-solving tasks.

🛠 How to Use

🔥 Ollama (Quick Inference)

You can run the model using Ollama for direct testing:

ollama run hf.co/ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp-Q4_K_M-GGUF

🤗 Hugging Face Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ZeroXClem/Qwen2.5-7B-HomerFuse-NerdExp"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

# Initialize text generation pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example prompt
prompt = "Describe the significance of AI ethics in modern technology."

# Generate output
outputs = text_generator(
    prompt,
    max_new_tokens=200,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

print(outputs[0]["generated_text"])

🏆 Performance & Benchmarks

This model has been crafted to perform exceptionally well across a variety of domains, including reasoning, mathematics, and conversation. Evaluation results will be updated upon testing.

🔥 Usage Recommendations

For best performance, ensure that you:

Use the correct tokenizer: allknowingroger/HomerSlerp6-7B
Fine-tune prompts for logical reasoning with a step-by-step approach.
Utilize the model in an interactive setting for long-form discussions.

🎯 Future Plans

🚀 Further optimization for multi-turn dialogues and zero-shot reasoning.
🧠 Improving knowledge distillation for factual consistency.
🎭 Enhancing character roleplay depth with better expressiveness.