Instructions to use ClaudioItaly/Evolutiongutenberg-50k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/Evolutiongutenberg-50k with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/Evolutiongutenberg-50k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Evolutiongutenberg-50k")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Evolutiongutenberg-50k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ClaudioItaly/Evolutiongutenberg-50k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/Evolutiongutenberg-50k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/Evolutiongutenberg-50k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ClaudioItaly/Evolutiongutenberg-50k

SGLang

How to use ClaudioItaly/Evolutiongutenberg-50k 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/Evolutiongutenberg-50k" \
    --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/Evolutiongutenberg-50k",
		"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/Evolutiongutenberg-50k" \
        --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/Evolutiongutenberg-50k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ClaudioItaly/Evolutiongutenberg-50k with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/Evolutiongutenberg-50k
```

Evolutiongutenberg-50k

File size: 1,006 Bytes

---
base_model:
- sanps/mist-7b-sft-gutenberg-50k
- ClaudioItaly/Evolutionstory-7B-v2.2
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 SLERP merge method.

### Models Merged

The following models were included in the merge:
* [sanps/mist-7b-sft-gutenberg-50k](https://huggingface.co/sanps/mist-7b-sft-gutenberg-50k)
* [ClaudioItaly/Evolutionstory-7B-v2.2](https://huggingface.co/ClaudioItaly/Evolutionstory-7B-v2.2)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: ClaudioItaly/Evolutionstory-7B-v2.2
  - model: sanps/mist-7b-sft-gutenberg-50k
merge_method: slerp
base_model: ClaudioItaly/Evolutionstory-7B-v2.2
dtype: bfloat16
parameters:
  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers

```