Instructions to use Phoenix21/fine-tuned-meditation-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phoenix21/fine-tuned-meditation-model with Transformers:

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

pipe = pipeline("text-generation", model="Phoenix21/fine-tuned-meditation-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Phoenix21/fine-tuned-meditation-model")
model = AutoModelForMultimodalLM.from_pretrained("Phoenix21/fine-tuned-meditation-model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Phoenix21/fine-tuned-meditation-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phoenix21/fine-tuned-meditation-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phoenix21/fine-tuned-meditation-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Phoenix21/fine-tuned-meditation-model

SGLang

How to use Phoenix21/fine-tuned-meditation-model 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 "Phoenix21/fine-tuned-meditation-model" \
    --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": "Phoenix21/fine-tuned-meditation-model",
		"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 "Phoenix21/fine-tuned-meditation-model" \
        --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": "Phoenix21/fine-tuned-meditation-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Phoenix21/fine-tuned-meditation-model with Docker Model Runner:
```
docker model run hf.co/Phoenix21/fine-tuned-meditation-model
```

fine-tuned-meditation-model / README.md

Phoenix21

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	---
	license: mit
	library_name: transformers
	tags:
	- text-generation
	- meditation
	---

	# Fine-Tuned Meditation Text Generation Model

	This model is fine-tuned for generating text related to meditation and mindfulness topics. It is compatible with the Hugging Face Transformers library and is optimized for text generation tasks.

	## Intended Use

	This model is designed to assist users by generating informative or calming text related to meditation, mindfulness, and relaxation practices. It can be used to create content for meditation guides, descriptions, or other wellness-oriented resources.

	## Example Usage with Hugging Face Transformers

	To use this model for text generation, you can load it directly with the Hugging Face `pipeline` and generate responses based on prompts related to meditation and mindfulness.

	### Code Example

	Install the required libraries if you haven’t already:

	```bash
	pip install transformers torch

	from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

	# Load the model and tokenizer
	model_name = "Phoenix21/fine-tuned-meditation-model" # Replace with your model path on Hugging Face
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	# Create a text generation pipeline
	generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

	# Example prompt
	prompt = "Meditation is a powerful tool for managing stress because"
	output = generator(prompt, max_length=100, do_sample=True, temperature=0.7)

	# Print generated text
	print(output[0]["generated_text"])