Instructions to use domamostafa/Fitness-Assistance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use domamostafa/Fitness-Assistance with Transformers:

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

pipe = pipeline("text-generation", model="domamostafa/Fitness-Assistance")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("domamostafa/Fitness-Assistance")
model = AutoModelForCausalLM.from_pretrained("domamostafa/Fitness-Assistance")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use domamostafa/Fitness-Assistance with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/domamostafa/Fitness-Assistance

SGLang

How to use domamostafa/Fitness-Assistance 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 "domamostafa/Fitness-Assistance" \
    --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": "domamostafa/Fitness-Assistance",
		"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 "domamostafa/Fitness-Assistance" \
        --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": "domamostafa/Fitness-Assistance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use domamostafa/Fitness-Assistance with Docker Model Runner:
```
docker model run hf.co/domamostafa/Fitness-Assistance
```

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

Developed by: Adham
Model type: Instruction-tuned chatbot (causal LM)
Language(s) (NLP): English
License: LLaMA 3.2 Community License
Finetuned from model: meta-llama/Llama-3.2-3B

Uses

Direct Use

This model is intended to serve as an intelligent assistant within a fitness application. It helps users by providing personalized fitness advice, answering health-related questions, and recommending routines or meals.

Out-of-Scope Use

Not suitable for medical diagnosis, mental health counseling, or advice on serious conditions. Avoid using it in high-risk applications.

Training Details

Training Data

[19,000 instruction-response pairs about fitness, nutrition, and health. Data was cleaned, normalized, and tokenized to fit LLaMA format.]

Preprocessing [optional]

[Normalization, de-duplication, and token formatting.]

Training Hyperparameters

Training regime: [Fine-tuning method: Parameter-efficient LoRA (r=8, alpha=16, dropout=0.05), applied to q_proj, k_proj, v_proj, o_proj.]

Speeds, Sizes, Times [optional]

Quantization: 4-bit using bitsandbytes

Optimizer: AdamW (β1=0.9, β2=0.999, ε=1e-8)

Training time: ~441 seconds

Total FLOPs: 1.53 quadrillion

Evaluation

Testing Data, Factors & Metrics

Metrics

train_loss: 1.5597

mean_token_accuracy: 65.45% (first 100 steps)

Testing Data

Not explicitly tested yet – training metrics only.

Summary

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [Insert your GPU/CPU model]
Hours used: [<1 hour]
Compute Region: [Your cloud provider or local]
Carbon Emitted: [Use MLCO2 Calculator] Tokenizerfile link https://drive.google.com/drive/folders/1aZevH_EC7FsfCm4vQnYScTCiG_SCPcIC?usp=sharing