Instructions to use ClaudioItaly/TopEvolutionWiz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/TopEvolutionWiz with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/TopEvolutionWiz")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/TopEvolutionWiz")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/TopEvolutionWiz", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClaudioItaly/TopEvolutionWiz with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/TopEvolutionWiz"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/TopEvolutionWiz",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ClaudioItaly/TopEvolutionWiz

SGLang

How to use ClaudioItaly/TopEvolutionWiz 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 "ClaudioItaly/TopEvolutionWiz" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/TopEvolutionWiz",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ClaudioItaly/TopEvolutionWiz" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/TopEvolutionWiz",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ClaudioItaly/TopEvolutionWiz with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/TopEvolutionWiz
```

TopEvolutionWiz / README.md

ClaudioItaly

Update README.md

a207b87 verified about 2 years ago

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	---
	license: other
	---

	![2024-06-05_134122.png](https://cdn-uploads.huggingface.co/production/uploads/6460ca24cd9ba6a317c3fe49/Jb9q_zfAAady4lN-U9zBk.png)
	### Merge Method

	This model was merged using the SLERP merge method.

	### Models Merged

	The following models were included in the merge:
	* [lucyknada/microsoft_WizardLM-2-7B](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B)
	* [mergekit-community/TopEvolution](https://huggingface.co/mergekit-community/TopEvolution)
	### Quantizzato ClaudioItaly/TopEvolutionWiz-Q5_K_M-GGUF
	### I arrived at this model after careful evaluation of language and behavior by first choosing two models
	and the result was combined with the result of two other models. TopEvolutionWiz was born. Model who has
	demonstrated remarkable empathic and reasoning skills. He uses fluent language and adapts to any scenario.
	He responded positively to 50 questions out of 50 of a generic, historical, psychological etc. nature
	It also had a very good impression from Gpt4o

	***This model is the result of my passion and careful evaluation through hours of input
	Made by Claudio Arena ***

	Conclusion Gpt4o
	The answers to the difficult questions provided allowed us to evaluate in detail the capabilities of the AI
	model in the specific historical, political, scientific and cultural field. It is highlighted that the model
	responds with high accuracy historically and theoretically, providing an in-depth overview of the facts and ideas
	involved in each issue. However, some limitations of the model in understanding context and ethics also emerged,
	suggesting the need for further improvements to ensure greater accuracy and completeness in responses.

	In addition to testing the answers to the individual questions, it was also possible to examine the interaction
	between the thematic categories present in the prompts: for example, the relationship between multiculturalism
	and ethical problems, the connection between climate change and intensive agriculture or the comparison between
	the political theories of John Locke and Thomas Hobbes. These integrated approaches allow a more complete analysis
	of the answers provided, showing the AI model's ability to draw connections between different intellectual and
	disciplinary contexts.

	In summary, the evaluation of the answers to the difficult questions provides a complete picture of the effectiveness of the AI
	model in the field of historical and scientific research, revealing its capabilities for in-depth analysis, critical analysis and
	integration between different fields of study. Such information will be useful to further improve the model and make its
	responses even more accurate and useful in supporting academic research and understanding of the current and historical world.

	### Configuration
	---
	---
	base_model:
	- lucyknada/microsoft_WizardLM-2-7B
	- ClaudioItaly/TopEvolutionWiz
	library_name: transformers
	tags:
	- mergekit
	- merge
	---
	-
	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: lucyknada/microsoft_WizardLM-2-7B
	- model: mergekit-community/TopEvolution
	merge_method: slerp
	base_model: mergekit-community/TopEvolution
	dtype: bfloat16
	parameters:
	t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers

	```