Instructions to use Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct")
model = AutoModelForCausalLM.from_pretrained("Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct", device_map="auto")
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 Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct

SGLang

How to use Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct 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 "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct" \
    --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": "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
		"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 "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct" \
        --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": "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct with Docker Model Runner:
```
docker model run hf.co/Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-4B-FitGPT-AR-EN-Instruct

Overview

Qwen3-4B-FitGPT-AR-EN-Instruct is a specialized bilingual fitness AI model, fine-tuned on top of a custom-merged Qwen3-4B foundation. It is designed to deliver science-based, practical fitness and nutrition guidance in both Arabic and English, while maintaining strict instruction-following capabilities including structured JSON output for agent-based systems.

This is the full merged model (LoRA adapters merged into 16-bit weights), ready for direct deployment without any additional adapter loading.

Model Details

Property	Value
Developed by	Mohamed Ramadan
Model Type	Causal Language Model (Custom Merged Base + Fine-tuned)
Base Architecture	Custom DARE-TIES Merge of `Qwen3-4B-Instruct-2507` + `Qwen3-4B`
Model Format	Full Weights — LoRA adapters merged into 16-bit base
Languages	Arabic 🇸🇦 & English 🇺🇸
Training Framework	Unsloth + Hugging Face TRL
License	Apache 2.0

Key Capabilities

🏋️ Bilingual Fitness Expert

Delivers detailed, science-backed advice on:

Workout programming & periodization
Macro/micro nutrition planning
Exercise technique and form cues
Recovery and injury prevention

🤖 Strict Agent / JSON Mode

The model is trained to follow formatting instructions precisely:

Returns only valid JSON when asked — no markdown wrappers, no preamble
Returns only a number when asked for a number
Returns only a list when asked for a list
Never adds unsolicited commentary

🌍 Arabic-Native Support

Unlike most fitness models that treat Arabic as an afterthought, this model was fine-tuned with a dedicated Arabic fitness corpus (CIDAR + alpaca-gpt4-arabic + custom data), enabling fluent, natural Arabic responses.

Training Pipeline

The model was developed through a 3-stage engineering pipeline:

Stage 1 — Foundation Merging
  Qwen3-4B-Instruct-2507 (55–70%)
          +
  Qwen3-4B Base (30–45%)
  ─────────────────────────────
  Method: DARE-TIES (layer-wise weights)
  Result: Custom bilingual base

Stage 2 — Supervised Fine-Tuning
  ~7,000 curated samples
  Curriculum-ordered (easy → hard)
  LoRA: r=64, alpha=128
  Framework: Unsloth + TRL (SFT)

Stage 3 — Weight Integration
  LoRA adapters merged into 16-bit base
  Result: Standalone deployment-ready model

Training Dataset Composition (~7,000 samples)

Source	Domain	Language
chibbss/fitness-chat	Fitness Q&A	EN
onurSakar/GYM-Exercise	Exercise library	EN
its-myrto/fitness-QA	Fitness Q&A	EN
Varick/workout-routine	Workout programs	EN
hammam/fitness-qa	Fitness synthetic	EN
arbml/CIDAR	General instructions	AR
alpaca-gpt4-arabic	General conversation	AR
mlabonne/FineTome-100k	Complex instructions	EN
Custom Agent examples	JSON / strict format	EN + AR

All samples passed quality filters (deduplication, min-length, response quality) and were curriculum-sorted from easiest to hardest before training.

System Prompts

For best results, use one of these system prompts:

Fitness Coach (English):

You are Qwen3-4B-FitGPT-AR-EN-Instruct, an elite fitness coach and sports nutritionist. Give science-based, detailed, personalised advice on training, nutrition, exercise technique, and recovery. Be specific and practical.

Fitness Coach (Arabic):

أنت Qwen3-4B-FitGPT-AR-EN-Instruct، مدرب لياقة بدنية نخبة وأخصائي تغذية رياضية. تقدّم نصائح علمية دقيقة ومخصصة في التدريب والتغذية وأداء التمارين والتعافي. كن تفصيلياً وعملياً ومستنداً إلى أحدث الأبحاث العلمية.

Agent / Strict JSON Mode:

You are a precise AI assistant. Follow every instruction exactly. If asked for JSON output — ONLY valid JSON, no markdown, no explanation, no text before or after. If asked for a number, return only the number. Never add unsolicited commentary.

How to Use

Option A — Transformers (Local)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

system = "You are an elite fitness coach. Give science-based, practical advice."
user   = "Create a weekly muscle-building plan for a 25-year-old male, 80 kg, beginner."

messages = [
    {"role": "system", "content": system},
    {"role": "user",   "content": user}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Option B — Unsloth (Faster, 4-bit)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Option C — Agent / JSON Output

import json

AGENT_SYSTEM = (
    "You are a precise AI assistant. Follow every instruction exactly. "
    "If asked for JSON output — ONLY valid JSON, no markdown, no explanation. "
    "Never add unsolicited commentary."
)

messages = [
    {"role": "system", "content": AGENT_SYSTEM},
    {"role": "user",   "content": "Return ONLY JSON: {exercise, sets, reps} for barbell squats."}
]

# ... generate as above, then:
response = model_generate(messages, temperature=0.1)  # low temp for JSON
data = json.loads(response)  # ✅ clean, parseable JSON

Example Outputs

Arabic fitness plan:

سؤال: أنا مبتدئ عمري 25 وزني 85 كغ طولي 178. أريد خطة تدريبية أسبوعية.

الموديل يرد بخطة تفصيلية بالعربي مع التمارين والتكرارات والتغذية المناسبة.

Strict JSON:

Prompt: Return ONLY JSON {exercise, sets, reps} for squats.

Output: {"exercise": "Barbell Squat", "sets": 4, "reps": 8}

Number-only:

Prompt: How many grams of protein per kg for a strength athlete? Return only the integer.

Output: 2

Related Repositories

Repo	Description
🔗 Qwen3-4B-FitGPT-AR-EN-Instruct	This repo — full 16-bit model
⚡ Qwen3-4B-FitGPT-AR-EN-Instruct-GGUF	Q4_K_M quantized — for Ollama & llama.cpp

Built with ❤️ by Mohamed Ramadan using Unsloth + Hugging Face

Downloads last month: 39

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(2134)

this model

Quantizations

2 models

Mohamed132411
/

Qwen3-4B-FitGPT-AR-EN-Instruct