Update README.md

README.md

@@ -1,199 +1,180 @@
 ---
+license: apache-2.0
+language:
+- tr
+- en
+tags:
+- phi3
+- causal-lm
+- text-generation
+- turkish
+- multilingual
+- code
+- from-scratch
+datasets:
+- uonlp/CulturaX
+- HuggingFaceTB/cosmopedia
+- roneneldan/TinyStories
+- nampdn-ai/tiny-textbooks
+- nampdn-ai/tiny-codes
+- ise-uiuc/Magicoder-Evol-Instruct-110K
+- theblackcat102/evol-codealpaca-v1
+- turkish-nlp-suite/InstrucTurca
+pipeline_tag: text-generation
 library_name: transformers
-tags: []
 ---
 
-# Model Card for Model ID
+# SykoLLM-V5.7-Mini
 
-<!-- Provide a quick summary of what the model is/does. -->
+SykoLLM-V5.7-Mini, sıfırdan (from scratch) eğitilmiş, Türkçe ve İngilizce destekli, kod anlama kapasitesine sahip küçük ölçekli bir dil modelidir. Phi-3 mimarisi temel alınarak özel BPE tokenizer ile geliştirilmiştir.
 
+---
 
+## Model Mimarisi
+
+| Özellik | Değer |
+|---|---|
+| Mimari | Phi-3 (Phi3ForCausalLM) |
+| Toplam Parametre | ~277 Milyon |
+| Gizli Katman Boyutu | 1024 |
+| Katman Sayısı | 18 |
+| Attention Head | 8 (2 KV Head — GQA) |
+| Vocabulary Boyutu | 50.000 |
+| Maksimum Bağlam | 1024 Token |
+| Aktivasyon Fonksiyonu | SiLU |
+| Eğitim Adımı | ~3.900 Step |
+| Yaklaşık Eğitim Örneği | ~512.000+ |
+| Eğitim Donanımı | 2x NVIDIA Tesla T4 |
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
+---
 
-## Model Examination [optional]
+## Tokenizer
 
-<!-- Relevant interpretability work for the model goes here -->
+Model, sıfırdan eğitilmiş özel bir BPE tokenizer kullanmaktadır. Hugging Face'in hazır tokenizer'larından bağımsız olarak geliştirilmiştir.
 
-[More Information Needed]
+- **Tür:** Byte-Level BPE
+- **Vocabulary Boyutu:** 50.000
+- **Normalizer:** NFKC
+- **Özel Token'lar:** `<|endoftext|>`, `<|user|>`, `<|assistant|>`, `<|end|>`, `<|pad|>`
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Eğitim Verisi
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+| Veri Seti | İçerik | Dil |
+|---|---|---|
+| uonlp/CulturaX | Genel Türkçe web metni | Türkçe |
+| HuggingFaceTB/cosmopedia | Sentetik eğitim materyali | İngilizce |
+| roneneldan/TinyStories | Kısa hikayeler | İngilizce |
+| nampdn-ai/tiny-textbooks | Sentetik ders kitabı | İngilizce |
+| nampdn-ai/tiny-codes | Kod örnekleri | Kod |
+| ise-uiuc/Magicoder-Evol-Instruct-110K | Kod instruction | Kod |
+| theblackcat102/evol-codealpaca-v1 | Kod instruction | Kod |
+| turkish-nlp-suite/InstrucTurca | Türkçe instruction | Türkçe |
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+---
 
-## Technical Specifications [optional]
+## Eğitim Detayları
+
+| Parametre | Değer |
+|---|---|
+| Optimizer | AdamW 8-bit (bitsandbytes) |
+| Learning Rate | 3e-4 |
+| LR Scheduler | Cosine |
+| Warmup Steps | 200 |
+| Batch Size | 8 per device x 2 GPU |
+| Gradient Accumulation | 8 (Efektif batch: 128) |
+| Max Steps | 3.900 |
+| Precision | FP16 |
+| Max Grad Norm | 1.0 |
+| Weight Decay | 0.05 |
 
-### Model Architecture and Objective
+---
 
-[More Information Needed]
+## Kullanım
 
-### Compute Infrastructure
+### Kurulum
 
-[More Information Needed]
+```bash
+pip install transformers torch
+```
 
-#### Hardware
+### Metin Üretimi
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-#### Software
+model_id = "SykoSLM/SykoLLM-V5.7-Mini"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
 
-## Citation [optional]
+prompt = "Türkiye'nin başkenti"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id
+    )
 
-**BibTeX:**
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-[More Information Needed]
+### Sohbet Formatı
 
-**APA:**
+Model `<|user|>` / `<|assistant|>` prompt formatıyla eğitilmiştir:
 
-[More Information Needed]
+```python
+prompt = "<|user|>\nPython'da Fibonacci dizisini nasıl yazarım?<|end|>\n<|assistant|>\n"
 
-## Glossary [optional]
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=300,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## Sınırlamalar
 
-## More Information [optional]
+- Model araştırma ve geliştirme amaçlıdır; production ortamı için önerilmez.
+- Eğitim adımı görece az olduğundan uzun ve karmaşık akıl yürütme görevlerinde hata yapabilir.
+- Maksimum bağlam uzunluğu 1024 token ile sınırlıdır.
+- Model, zararlı içerik filtrelemesi için hizalanmamıştır (RLHF/DPO uygulanmamıştır).
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## Lisans
 
-[More Information Needed]
+Bu model Apache 2.0 lisansı altında yayınlanmıştır.
 
-## Model Card Contact
+---
 
-[More Information Needed]
+## Alıntı
+
+```bibtex
+@misc{sykollm-v57-mini-2025,
+  author    = {SykoSLM},
+  title     = {SykoLLM-V5.7-Mini: A Small Multilingual Causal Language Model Trained from Scratch},
+  year      = {2025},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/SykoSLM/SykoLLM-V5.7-Mini}
+}
+```