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

Libraries

How to use ClaudioItaly/EvolutiontRock with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/EvolutiontRock")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/EvolutiontRock")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClaudioItaly/EvolutiontRock with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ClaudioItaly/EvolutiontRock

SGLang

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

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

EvolutiontRock / README.md

ClaudioItaly

Upload folder using huggingface_hub

521a88a verified about 2 years ago

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	---
	base_model:
	- ClaudioItaly/TopEvolutionWiz
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the passthrough merge method.

	### Models Merged

	The following models were included in the merge:
	* [ClaudioItaly/TopEvolutionWiz](https://huggingface.co/ClaudioItaly/TopEvolutionWiz)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	slices:
	- sources:
	- layer_range: [0, 6]
	model: ClaudioItaly/TopEvolutionWiz
	- sources:
	- layer_range: [6, 12]
	model: ClaudioItaly/TopEvolutionWiz
	- sources:
	- layer_range: [12, 18]
	model: ClaudioItaly/TopEvolutionWiz
	- sources:
	- layer_range: [18, 24]
	model: ClaudioItaly/TopEvolutionWiz
	- sources:
	- layer_range: [24, 30]
	model: ClaudioItaly/TopEvolutionWiz
	merge_method: passthrough
	dtype: float16


	```