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Model: turkerberkdonmez/TUSGPT-TR-Medical-9B
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+TUSGPT-TR-Medical-9B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language:
+  - tr
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model: ytu-ce-cosmos/Turkish-Gemma-9b-T1
+datasets:
+  - turkerberkdonmez/TUSGPT-TR-Medical-Dataset-v1
+tags:
+  - medical
+  - turkish
+  - gemma2
+  - dora
+  - sft
+  - mlx
+  - apple-silicon
+model-index:
+  - name: TUSGPT-TR-Medical-9B
+    results: []
+---
+
+<div align="center">
+
+# ⚕️ TUSGPT-TR-Medical-9B
+
+**Türkiye'nin İlk Açık Kaynak Türkçe Medikal Dil Modeli** *Turkey's First Open-Source Turkish Medical Language Model*
+
+[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Base Model](https://img.shields.io/badge/Base_Model-Gemma_2_9B-blue)](https://huggingface.co/ytu-ce-cosmos/Turkish-Gemma-9b-T1)
+[![Language](https://img.shields.io/badge/Language-Turkish-red)](https://huggingface.co/languages/tr)
+[![Framework](https://img.shields.io/badge/Training-MLX-purple)](https://github.com/ml-explore/mlx)
+
+![tusgpt_readme_banner_horizontal](https://cdn-uploads.huggingface.co/production/uploads/644f952123d7eb05ca699d31/cAwybl8lXJsIiSYnkj5IG.png)
+
+[🇹🇷 Türkçe Açıklama](#-model-hakkında) | [🇬🇧 English Description](#-model-description) | [💻 Kullanım/Usage](#-kullanım--usage)
+
+</div>
+
+---
+
+## 🇹🇷 Model Hakkında
+
+**TUSGPT-TR-Medical-9B**, Türkiye'nin medikal alandaki yapay zeka gelişimine katkı sağlamak amacıyla geliştirilmiş, **Gemma-2** mimarisine dayalı 9 milyar parametreli bir dil modelidir.
+
+Model, **[ytu-ce-cosmos/Turkish-Gemma-9b-T1](https://huggingface.co/ytu-ce-cosmos/Turkish-Gemma-9b-T1)** temel modeli üzerine, **55.000'den fazla yüksek kaliteli Türkçe tıbbi soru-cevap çifti** ile **2 aşamalı DoRA (Weight-Decomposed Low-Rank Adaptation)** yöntemi kullanılarak fine-tune edilmiştir.
+
+### 📚 Veri Seti Kapsamı (Dataset Coverage)
+
+Model, aşağıdaki branşları ve daha fazlasını kapsayan **55,465 Türkçe soru-cevap** çifti ile eğitilmiştir:
+
+* **Temel Bilimler:** Farmakoloji, Patoloji, Anatomi, Fizyoloji  
+* **Klinik Bilimler:** Dahiliye, Cerrahi, Pediatri, Kadın Doğum  
+* **Diğer:** Acil Tıp, Nöroloji, Onkoloji, Radyoloji  
+
+---
+
+## 🇬🇧 Model Description
+
+**TUSGPT-TR-Medical-9B** is a specialized 9-billion parameter language model based on the **Gemma-2** architecture, designed to advance medical AI research in Turkey.
+
+It is fine-tuned on the **[ytu-ce-cosmos/Turkish-Gemma-9b-T1](https://huggingface.co/ytu-ce-cosmos/Turkish-Gemma-9b-T1)** base model using **55,000+ high-quality Turkish medical Q&A pairs**, with a **2-Stage DoRA** methodology trained on Apple Silicon hardware.
+
+### 📚 Dataset Scope
+
+The model covers a wide range of medical disciplines with **55,465 Q&A pairs**, including:
+
+* **Basic Sciences:** Pharmacology, Pathology, Anatomy, Physiology  
+* **Clinical Sciences:** Internal Medicine, Surgery, Pediatrics, Obstetrics & Gynecology  
+* **Others:** Emergency Medicine, Neurology, Oncology, Radiology  
+
+---
+
+## 📊 Teknik Detaylar / Technical Details
+
+| Özellik / Feature | Detay / Detail |
+|---|---|
+| **Base Model** | [ytu-ce-cosmos/Turkish-Gemma-9b-T1](https://huggingface.co/ytu-ce-cosmos/Turkish-Gemma-9b-T1) |
+| **Architecture** | Gemma 2 (9.24B Parameters) |
+| **Dataset** | [turkerberkdonmez/TUSGPT-TR-Medical-Dataset-v1](https://huggingface.co/datasets/turkerberkdonmez/TUSGPT-TR-Medical-Dataset-v1) |
+| **Dataset Size** | 55,465 samples (Q&A) |
+| **Training Method** | 2-Stage DoRA (Weight-Decomposed LoRA) |
+| **Precision** | bfloat16 |
+| **Hardware** | Apple Mac Studio (M-Series, 128GB Unified Memory) |
+
+<details>
+<summary><strong>🔬 Eğitim Parametrelerini Görüntüle / View Training Hyperparameters</strong></summary>
+
+### Stage 1 — Aggressive Knowledge Injection
+* **DoRA:** rank=64, alpha=128, target_modules=all linear layers  
+* **Optimizer:** NEFTune (alpha=3) enabled  
+* **Learning Rate:** 2e-5 → 2e-6 (cosine decay)  
+* **Steps:** 1600 iterations (~1 epoch)
+
+### Stage 2 — Stabilization
+* **Config:** Resumed from Stage 1 best checkpoint  
+* **Optimizer:** NEFTune disabled  
+* **Learning Rate:** 5e-6 → 1e-7  
+* **Steps:** 1000 iterations  
+* **Final Val Loss:** 1.126  
+</details>
+
+---
+
+## 💻 Kullanım / Usage
+
+### 🐍 Python (Transformers)
+
+> **Generation önerisi:** Temperature=0.6, TopP=0.95, TopK=20, MinP=0 (generation_config.json varsayılanı).  
+> **Greedy decoding kullanmayın**; performans düşüşüne ve sonsuz tekrarlara yol açabilir.  
+> **Complex tasks:** `max_new_tokens` değerini artırın. Gerekirse `repetition_penalty` ve `presence_penalty` (0–2) ayarlanabilir.  
+> Not: Daha yüksek değerler bazen dil karışmasına ve hafif performans düşüşüne neden olabilir.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "turkerberkdonmez/TUSGPT-TR-Medical-9B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "Sen tıp alanında uzmanlaşmış, Türkçe yanıt veren bir yapay zeka asistanısın. Soruları doğru, kapsamlı ve anlaşılır biçimde yanıtla."},
+    {"role": "user", "content": "Akut miyokard enfarktüsünün erken belirtileri nelerdir?"},
+]
+
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,     # complex tasks için artırılabilir
+    temperature=0.6,
+    top_p=0.95,
+    top_k=20,
+    # min_p Transformers'ta her zaman desteklenmeyebilir; model config'ine bağlıdır.
+    # Aşağıdakiler opsiyonel: tekrarları azaltmak için
+    # repetition_penalty=1.15,
+    # presence_penalty=0.3,
+    do_sample=True,         # greedy decoding kapalı
+)
+
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+### 🦙 GGUF (Ollama & LM Studio)
+
+Bu modelin sıkıştırılmış (quantized) versiyonları yerel cihazlarda çalıştırılabilir.
+
+| Dosya Adı (Filename)               | Quant  | Boyut (Size) | Önerilen Donanım (Recommended HW) |
+| ---------------------------------- | ------ | ------------ | --------------------------------- |
+| `TUSGPT-TR-Medical-9B-Q8_0.gguf`   | Q8_0   | ~9.8 GB      | 12GB+ VRAM / 16GB+ RAM            |
+| `TUSGPT-TR-Medical-9B-Q4_K_M.gguf` | Q4_K_M | ~5.8 GB      | 8GB+ VRAM / 12GB+ RAM             |
+
+#### Ollama Setup
+
+> **Önerilen üretim ayarları:** Temperature=0.6, TopP=0.95, TopK=20, MinP=0  
+> **Greedy decoding kullanmayın**; performans düşüşü ve sonsuz tekrar riski yaratabilir.  
+> **Complex tasks:** `num_predict` (max_new_tokens) artırılabilir.  
+> Tekrarlar olursa `repeat_penalty` ve `presence_penalty` (0–2) ayarlanabilir (yüksek değerler bazen dil karışmasına ve hafif performans düşüşüne yol açabilir).
+
+1. **Modelfile Oluşturun / Create Modelfile:**
+
+```dockerfile
+FROM ./TUSGPT-TR-Medical-9B-Q4_K_M.gguf
+
+SYSTEM "Sen tıp alanında uzmanlaşmış, Türkçe yanıt veren bir yapay zeka asistanısın. Soruları doğru, kapsamlı ve anlaşılır biçimde yanıtla."
+
+# Recommended generation (DO NOT use greedy decoding)
+PARAMETER temperature 0.6
+PARAMETER top_p 0.95
+PARAMETER top_k 20
+PARAMETER min_p 0
+
+# Complex tasks: increase num_predict
+# PARAMETER num_predict 1024
+
+# To reduce endless repetitions (optional; tune gradually)
+# PARAMETER repeat_penalty 1.15
+# PARAMETER presence_penalty 0.3
+```
+
+2. **Modeli Çalıştırın / Run Model:**
+
+```bash
+ollama create tusgpt-medical -f Modelfile
+ollama run tusgpt-medical
+```
+
+---
+
+## ⚠️ Yasal Uyarı / Disclaimer
+
+#### 🇹🇷 Türkçe
+
+> **Bu model eğitim ve araştırma amaçlıdır.** Klinik karar verme süreçlerinde tek başına kullanılmamalıdır. Tıbbi kararlar için her zaman uzman hekime danışın.
+
+#### 🇬🇧 English
+
+> **This model is for educational and research purposes only.** It should not be used as a sole source for clinical decision-making. Always consult a qualified physician for medical decisions.
+
+---
+
+## 🤝 Acknowledgments
+
+* **Base Model:** [YTU CE COSMOS Lab](https://huggingface.co/ytu-ce-cosmos) — Turkish-Gemma-9b-T1
+* **Training Framework:** [MLX](https://github.com/ml-explore/mlx) by Apple
+
+---
+
+## 📝 Citation
+
+```bibtex
+@misc{tusgpt-tr-medical-9b,
+  title        = {TUSGPT-TR-Medical-9B: Turkish Medical Language Model},
+  author       = {Türker Berk Dönmez},
+  year         = {2026},
+  url          = {https://huggingface.co/turkerberkdonmez/TUSGPT-TR-Medical-9B}
+}
+```

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chat_template.jinja

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+{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
+' + message['content'] | trim + '<end_of_turn>
+' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
+'}}{% endif %}

config.json

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+    "architectures": [
+        "Gemma2ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_logit_softcapping": 50.0,
+    "bos_token_id": 2,
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+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 3584,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 8192,
+    "model_type": "gemma2",
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+    "num_hidden_layers": 42,
+    "num_key_value_heads": 8,
+    "pad_token_id": 0,
+    "query_pre_attn_scalar": 256,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 10000.0,
+    "sliding_window": 4096,
+    "sliding_window_size": 4096,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.51.3",
+    "use_cache": false,
+    "vocab_size": 256000
+}

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+  "transformers_version": "4.51.3",
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
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