Instructions to use Lukamac/PlayPart-AI-Personal-Trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lukamac/PlayPart-AI-Personal-Trainer with Transformers:

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

pipe = pipeline("text-generation", model="Lukamac/PlayPart-AI-Personal-Trainer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lukamac/PlayPart-AI-Personal-Trainer")
model = AutoModelForCausalLM.from_pretrained("Lukamac/PlayPart-AI-Personal-Trainer")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lukamac/PlayPart-AI-Personal-Trainer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Lukamac/PlayPart-AI-Personal-Trainer

SGLang

How to use Lukamac/PlayPart-AI-Personal-Trainer 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 "Lukamac/PlayPart-AI-Personal-Trainer" \
    --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": "Lukamac/PlayPart-AI-Personal-Trainer",
		"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 "Lukamac/PlayPart-AI-Personal-Trainer" \
        --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": "Lukamac/PlayPart-AI-Personal-Trainer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lukamac/PlayPart-AI-Personal-Trainer with Docker Model Runner:
```
docker model run hf.co/Lukamac/PlayPart-AI-Personal-Trainer
```

PlayPart-AI-Personal-Trainer / README.md

Lukamac

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	---
	license: apache-2.0
	datasets:
	- lizziepika/strava_activities_runs
	- Lukamac/MegaGym_dataset
	language:
	- en
	metrics:
	- perplexity
	- accuracy
	base_model:
	- openai-community/gpt2
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- gpt2
	- text-generation
	- sports
	- fitness
	- gym
	---

	# PlayPart AI Personal Trainer Model

	This model is a fine-tuned version of GPT-2, specifically trained on sports-related and gym exercise datasets. It is intended to provide text-generation capabilities for answering questions about fitness, sports, workout routines, and providing personalized training suggestions.

	## Intended Use
	- Text Generation: Generate text based on sports and fitness questions and interactions.
	- Personal Trainer Chatbot: Suitable for chatbot integrations focused on fitness, workouts, and sports topics.

	### Usage
	To use the model, you can either use the Hugging Face Inference API or load it in your Python environment.

	#### Example (Python)
	```python
	from transformers import GPT2Tokenizer, GPT2LMHeadModel

	# Load the model
	tokenizer = GPT2Tokenizer.from_pretrained("Lukamac/PlayPart-AI-Personal-Trainer")
	model = GPT2LMHeadModel.from_pretrained("Lukamac/PlayPart-AI-Personal-Trainer")

	# Generate a response
	input_text = "What are the best exercises for building upper body strength?"
	input_ids = tokenizer.encode(input_text, return_tensors='pt')
	output_ids = model.generate(input_ids, max_length=50)
	response = tokenizer.decode(output_ids[0], skip_special_tokens=True)

	print(response)