---
language:
- en
- ar
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- fitness
- arabic
- bilingual
- agent
- json-output
pipeline_tag: text-generation
license: apache-2.0
---

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

<p align="center">
  <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="180"/>
</p>

## 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)

```python
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)

```python
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

```python
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](https://huggingface.co/Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct) | This repo — full 16-bit model |
| ⚡ [Qwen3-4B-FitGPT-AR-EN-Instruct-GGUF](https://huggingface.co/Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct-GGUF) | Q4_K_M quantized — for Ollama & llama.cpp |

---

*Built with ❤️ by Mohamed Ramadan using Unsloth + Hugging Face*