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Model: Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct Source: Original Platform
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README.md
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---
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language:
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- en
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- ar
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base_model: Qwen/Qwen3-4B-Instruct-2507
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen3
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- fitness
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- arabic
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- bilingual
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- agent
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- json-output
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# Qwen3-4B-FitGPT-AR-EN-Instruct
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<p align="center">
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<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="180"/>
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</p>
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## Overview
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**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.
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This is the **full merged model** (LoRA adapters merged into 16-bit weights), ready for direct deployment without any additional adapter loading.
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---
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## Model Details
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| Property | Value |
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|----------|-------|
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| **Developed by** | Mohamed Ramadan |
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| **Model Type** | Causal Language Model (Custom Merged Base + Fine-tuned) |
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| **Base Architecture** | Custom DARE-TIES Merge of `Qwen3-4B-Instruct-2507` + `Qwen3-4B` |
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| **Model Format** | Full Weights — LoRA adapters merged into 16-bit base |
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| **Languages** | Arabic 🇸🇦 & English 🇺🇸 |
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| **Training Framework** | Unsloth + Hugging Face TRL |
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| **License** | Apache 2.0 |
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---
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## Key Capabilities
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### 🏋️ Bilingual Fitness Expert
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Delivers detailed, science-backed advice on:
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- Workout programming & periodization
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- Macro/micro nutrition planning
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- Exercise technique and form cues
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- Recovery and injury prevention
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### 🤖 Strict Agent / JSON Mode
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The model is trained to follow formatting instructions precisely:
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- Returns **only valid JSON** when asked — no markdown wrappers, no preamble
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- Returns **only a number** when asked for a number
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- Returns **only a list** when asked for a list
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- Never adds unsolicited commentary
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### 🌍 Arabic-Native Support
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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.
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---
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## Training Pipeline
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The model was developed through a **3-stage engineering pipeline**:
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```
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Stage 1 — Foundation Merging
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Qwen3-4B-Instruct-2507 (55–70%)
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+
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Qwen3-4B Base (30–45%)
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─────────────────────────────
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Method: DARE-TIES (layer-wise weights)
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Result: Custom bilingual base
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Stage 2 — Supervised Fine-Tuning
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~7,000 curated samples
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Curriculum-ordered (easy → hard)
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LoRA: r=64, alpha=128
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Framework: Unsloth + TRL (SFT)
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Stage 3 — Weight Integration
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LoRA adapters merged into 16-bit base
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Result: Standalone deployment-ready model
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```
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### Training Dataset Composition (~7,000 samples)
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| Source | Domain | Language |
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|--------|--------|----------|
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| chibbss/fitness-chat | Fitness Q&A | EN |
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| onurSakar/GYM-Exercise | Exercise library | EN |
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| its-myrto/fitness-QA | Fitness Q&A | EN |
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| Varick/workout-routine | Workout programs | EN |
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| hammam/fitness-qa | Fitness synthetic | EN |
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| arbml/CIDAR | General instructions | AR |
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| alpaca-gpt4-arabic | General conversation | AR |
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| mlabonne/FineTome-100k | Complex instructions | EN |
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| Custom Agent examples | JSON / strict format | EN + AR |
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All samples passed quality filters (deduplication, min-length, response quality) and were **curriculum-sorted** from easiest to hardest before training.
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---
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## System Prompts
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For best results, use one of these system prompts:
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**Fitness Coach (English):**
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```
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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.
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```
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**Fitness Coach (Arabic):**
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```
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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.
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```
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**Agent / Strict JSON Mode:**
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```
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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.
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```
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---
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## How to Use
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### Option A — Transformers (Local)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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system = "You are an elite fitness coach. Give science-based, practical advice."
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user = "Create a weekly muscle-building plan for a 25-year-old male, 80 kg, beginner."
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messages = [
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{"role": "system", "content": system},
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{"role": "user", "content": user}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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### Option B — Unsloth (Faster, 4-bit)
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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"Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct",
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max_seq_length=2048,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(model)
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```
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### Option C — Agent / JSON Output
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```python
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import json
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AGENT_SYSTEM = (
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"You are a precise AI assistant. Follow every instruction exactly. "
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"If asked for JSON output — ONLY valid JSON, no markdown, no explanation. "
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"Never add unsolicited commentary."
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)
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messages = [
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{"role": "system", "content": AGENT_SYSTEM},
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{"role": "user", "content": "Return ONLY JSON: {exercise, sets, reps} for barbell squats."}
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]
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# ... generate as above, then:
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response = model_generate(messages, temperature=0.1) # low temp for JSON
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data = json.loads(response) # ✅ clean, parseable JSON
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```
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---
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## Example Outputs
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**Arabic fitness plan:**
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> سؤال: أنا مبتدئ عمري 25 وزني 85 كغ طولي 178. أريد خطة تدريبية أسبوعية.
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>
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> *الموديل يرد بخطة تفصيلية بالعربي مع التمارين والتكرارات والتغذية المناسبة.*
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**Strict JSON:**
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> Prompt: `Return ONLY JSON {exercise, sets, reps} for squats.`
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>
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> Output: `{"exercise": "Barbell Squat", "sets": 4, "reps": 8}`
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**Number-only:**
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> Prompt: `How many grams of protein per kg for a strength athlete? Return only the integer.`
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>
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> Output: `2`
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---
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## Related Repositories
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| Repo | Description |
|
||||
|------|-------------|
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| 🔗 [Qwen3-4B-FitGPT-AR-EN-Instruct](https://huggingface.co/Mohamed132411/Qwen3-4B-FitGPT-AR-EN-Instruct) | This repo — full 16-bit model |
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| ⚡ [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 |
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---
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*Built with ❤️ by Mohamed Ramadan using Unsloth + Hugging Face*
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61
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
|
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{{- '<tool_call>\n{"name": "' }}
|
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{{- tool_call.name }}
|
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{{- '", "arguments": ' }}
|
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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||||
{{- '\n</tool_response>' }}
|
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
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{{- '<|im_end|>\n' }}
|
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"torch_dtype": "float16",
|
||||
"eos_token_id": 151645,
|
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"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
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"initializer_range": 0.02,
|
||||
"intermediate_size": 9728,
|
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"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 262144,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 5000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"unsloth_version": "2026.4.5",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
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3
model-00001-of-00002.safetensors
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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406
model.safetensors.index.json
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406
model.safetensors.index.json
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{
|
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"metadata": {
|
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"total_size": 8044936192,
|
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"mergekit_version": "0.1.4"
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"weight_map": {
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"model.layers.9.self_attn.q_norm.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:476870a1f2fb6f6a2759a6ede2383bf9d5d738f17844563b65c91965b722ae09
|
||||
size 11422924
|
||||
17
tokenizer_config.json
Normal file
17
tokenizer_config.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [],
|
||||
"is_local": true,
|
||||
"model_max_length": 262144,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}"
|
||||
}
|
||||
Reference in New Issue
Block a user