Instructions to use mergekit-community/TopEvolution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mergekit-community/TopEvolution with Transformers:

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

pipe = pipeline("text-generation", model="mergekit-community/TopEvolution")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mergekit-community/TopEvolution")
model = AutoModelForCausalLM.from_pretrained("mergekit-community/TopEvolution")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mergekit-community/TopEvolution with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mergekit-community/TopEvolution

SGLang

How to use mergekit-community/TopEvolution 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 "mergekit-community/TopEvolution" \
    --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": "mergekit-community/TopEvolution",
		"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 "mergekit-community/TopEvolution" \
        --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": "mergekit-community/TopEvolution",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mergekit-community/TopEvolution with Docker Model Runner:
```
docker model run hf.co/mergekit-community/TopEvolution
```

Update README.md

by ClaudioItaly - opened May 27, 2024

base: refs/heads/main

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from: refs/pr/2

Discussion Files changed

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ClaudioItaly

May 27, 2024

I am immensely satisfied to have created this model that demonstrates high capabilities in every task. Example with just one well-specified prompt,
he made a whole book of 10 chapters with 27 thousand tokens.
I also subjected it to 50 rigorous general knowledge questions. The result was 50 positive answers out of 50. GPT4o said about this model:
Conclusion
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.
my page https://huggingface.co/ClaudioItaly By Claudio Arena

This is a merge of pre-trained language models created using mergekit.

Update README.mda0165e76

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