SykoSLM
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SykoLLM-Phi3-Test

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-library_name: transformers
-license: apache-2.0
-language:
-- tr
-- en
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-# Model Card for Model ID
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+#  Burak Phi-3 Mini (233M) - Experimental
+> **⚠️ Uyarı:** Bu model şu anda aktif bir **test aşamasındadır**. Amacı, yeni nesil **Phi-3** mimarisinin yeteneklerini sıfırdan eğitilmiş bir yapıyla (from scratch) küçük ölçekte test etmektir. Üretime (production) hazır bir model değildir ve halüsinasyon görebilir veya beklenmedik yanıtlar verebilir.
+## Model Özeti
+Bu model, Microsoft'un **Phi-3** mimarisi temel alınarak sıfırdan eğitilmiş **233 Milyon** parametreli, deneysel bir dil modelidir. Hem İngilizce hem de Türkçe dillerinde genel bilgi, kodlama yeteneği ve mantıksal yürütme (reasoning) kapasitesini ölçmek amacıyla çeşitli yüksek kaliteli veri setleri harmanlanarak eğitilmiştir.
+Ayrıca model için `BPE` (Byte-Pair Encoding) tabanlı **özel bir tokenizer** sıfırdan eğitilmiş ve kullanılmıştır.
+##  Model Mimarisi Detayları
+Model, standart LLaMA/Phi yapılandırmalarına benzer ancak daha kompakt bir konfigürasyonda tasarlanmıştır:
+* **Mimari:** Phi-3 (`Phi3ForCausalLM`)
+* **Parametre Sayısı:** 233M
+* **Gömme Boyutu (Hidden Size):** 768
+* **Ara Katman Boyutu (Intermediate Size):** 2304
+* **Katman Sayısı (Hidden Layers):** 24
+* **Dikkat Başlıkları (Attention Heads):** 6 (GQA destekli)
+* **Bağlam Penceresi (Context Size):** 1024 Token
+* **Sözlük Boyutu (Vocab Size):** 32,000
+##  Eğitim Verisi (Datasets)
+Eğitim verisi, modelin çok yönlü olabilmesi için özenle seçilmiş, yapay zeka kalıntıları (AI-robots) temizlenmiş ve filtrelenmiş şu veri setlerinden oluşmaktadır:
+**Genel Bilgi ve Mantık (İngilizce & Hikaye):**
+* `HuggingFaceTB/cosmopedia` (Math, KhanAcademy, OpenStax, Stanford, Web Samples, WikiHow)
+* `roneneldan/TinyStories`
+* `nampdn-ai/tiny-textbooks`
+**Kodlama ve Talimat (Coding & Instruct):**
+* `nampdn-ai/tiny-codes`
+* `ise-uiuc/Magicoder-Evol-Instruct-110K`
+* `theblackcat102/evol-codealpaca-v1`
+**Türkçe Veriler:**
+* `uonlp/CulturaX` (Türkçe alt kümesi, temizlenmiş)
+* `turkish-nlp-suite/InstrucTurca`
+## Eğitim Konfigürasyonu
+* **Optimizasyon:** 8-bit AdamW (`adamw_bnb_8bit`)
+* **Öğrenme Oranı (Learning Rate):** 3e-4 (Cosine Scheduler ile)
+* **Hassasiyet (Precision):** FP16
+* **Max Adım Sayısı:** 6000
+## Örnek Kullanım (Inference)
+Modeli denemek için aşağıdaki Python kodunu kullanabilirsiniz:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "kullanici_adin/burak-phi3-233m" # Burayı kendi reponla değiştir
+# Modeli ve Tokenizer'ı yükle
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="auto")
+prompt = "<|user|>\nPython ile bir 'Hello World' yaz.\n<|end|>\n<|assistant|>\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=0.7,
+    do_sample=True,
+    eos_token_id=tokenizer.eos_token_id
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))