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

Libraries

How to use ClaudioItaly/Goodenberk with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/Goodenberk")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Goodenberk")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Goodenberk")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ClaudioItaly/Goodenberk with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/Goodenberk"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/Goodenberk",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ClaudioItaly/Goodenberk

SGLang

How to use ClaudioItaly/Goodenberk 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/Goodenberk" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/Goodenberk",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Goodenberk" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/Goodenberk",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Goodenberk / README.md

ClaudioItaly

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	---
	base_model:
	- AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b-sft-3epochs
	- Orion-zhen/Qwen2.5-7B-Gutenberg-KTO
	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:
	* [AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b-sft-3epochs](https://huggingface.co/AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b-sft-3epochs)
	* [Orion-zhen/Qwen2.5-7B-Gutenberg-KTO](https://huggingface.co/Orion-zhen/Qwen2.5-7B-Gutenberg-KTO)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml

	models:
	- model: Orion-zhen/Qwen2.5-7B-Gutenberg-KTO
	- model: AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b-sft-3epochs
	merge_method: slerp
	tokenizer_merge_method: slerp
	tokenizer_parameters:
	t: 0.3 # Dà più peso al tokenizer
	base_model: AlekseyKorshuk/ai-detection-gutenberg-human-choosed-formatted-ai-sft-qwen-7b-sft-3epochs
	dtype: bfloat16
	parameters:
	t: [0, 0.2, 0.4, 0.5, 0.4, 0.2, 0] # Curva che favorisce leggermente
	temp: 1.3 # Temperatura per smoothare il merge
	density: # Density merging per bilanciare le caratteristiche dei due modelli
	- threshold: 0.1
	t: 0.7
	- threshold: 0.5
	t: 0.5
	- threshold: 0.9
	t: 0.3

	```