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

Libraries

How to use Bogula/TildePink with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bogula/TildePink")
model = AutoModelForCausalLM.from_pretrained("Bogula/TildePink")
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 Bogula/TildePink with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Bogula/TildePink

SGLang

How to use Bogula/TildePink 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 "Bogula/TildePink" \
    --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": "Bogula/TildePink",
		"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 "Bogula/TildePink" \
        --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": "Bogula/TildePink",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

TildePink / README.md

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	---
	license: apache-2.0
	base_model: TildeAI/TildeOpen-30b
	language:
	- de
	tags:
	- fitness
	- coach
	- llama-3
	pipeline_tag: text-generation
	library_name: transformers
	---

	# TildePink 30B - SoftSkill-Coach-Alpha

	TildePink is a fine-tuned version of TildeOpen-30b, optimized to act as a professional German-speaking fitness coach.

	## Model Details
	- Developed by: Werner Bogula **
	- Language: German (Primary)
	- Persona: Motivating SoftskillCoach, answering concise and helpful
	- Base Model: TildeOpen-30b
	- Training Epochs: 3.0
	- Final Loss: ~0.41

	## Intended Use
	This model is designed to check how a base model can be finetuned to follow instructions