Instructions to use ClaudioItaly/Evocation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClaudioItaly/Evocation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ClaudioItaly/Evocation")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Evocation") model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Evocation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ClaudioItaly/Evocation with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ClaudioItaly/Evocation" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ClaudioItaly/Evocation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ClaudioItaly/Evocation
- SGLang
How to use ClaudioItaly/Evocation 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/Evocation" \ --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/Evocation", "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/Evocation" \ --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/Evocation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ClaudioItaly/Evocation with Docker Model Runner:
docker model run hf.co/ClaudioItaly/Evocation
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base_model:
- Undi95/Utopia-13B
- cognitivecomputations/WizardLM-1.0-Uncensored-Llama2-13b
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 della_linear merge method using [Undi95/Utopia-13B](https://huggingface.co/Undi95/Utopia-13B) as a base.
### Models Merged
The following models were included in the merge:
* [cognitivecomputations/WizardLM-1.0-Uncensored-Llama2-13b](https://huggingface.co/cognitivecomputations/WizardLM-1.0-Uncensored-Llama2-13b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: cognitivecomputations/WizardLM-1.0-Uncensored-Llama2-13b
parameters:
weight: 0.5
density: 0.8
- model: cognitivecomputations/WizardLM-1.0-Uncensored-Llama2-13b
parameters:
weight: 0.5
density: 0.8
merge_method: della_linear
base_model: Undi95/Utopia-13B
parameters:
epsilon: 0.05
lambda: 1
int8_mask: true
dtype: bfloat16
tokenzer_source: union
```
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