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 Settings

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

File size: 1,569 Bytes

50226a6

---
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

```