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

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metadata

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

merge

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

Configuration

The following YAML configuration was used to produce this model:

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