SykoSLM
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SykoLLM-V1.2-Turkish-Instruct

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-# Model Card for Model ID
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+# Model Card: SykoLLM-V1-Turkish
+SykoLLM-V1-Turkish is a custom-architected, lightweight Large Language Model (LLM) designed specifically for Turkish conversational tasks. Unlike standard pre-built models, this version features a custom configuration optimized for speed and efficiency in low-resource environments.
+## Model Description
+* **Developed by:** syko818121
+* **Model Name:** SykoLLM-V1-Turkish
+* **Model Type:** Causal Decoder-Only Custom Architecture
+* **Language:** Turkish
+* **Parameters:** ~50.8 Million
+* **Training Data:** Turkish Wikipedia + Custom High-Quality Chat Dataset
+## Architectural Specs
+This model uses a custom configuration designed for Turkish linguistics:
+* **Vocabulary Size:** 50,257
+* **Hidden Dimension (n_embd):** 512
+* **Number of Layers:** 8
+* **Attention Heads:** 8
+* **Context Window:** 512 tokens
+## Fine-Tuning & Conversation Style
+The model was fine-tuned on a high-quality, curated Turkish dataset  to ensure natural, human-like responses. The training data distribution was carefully balanced:
+*
+**Greetings & Daily Talk (40%):** Natural openings and casual conversation.
+*
+**Direct Question-Answering (30%):** Short and concise answers to general knowledge queries.
+*
+**Brief Explanations (20%):** Simplified definitions for complex concepts.
+*
+**Slang & Short Inputs (10%):** Robustness against one-word or incomplete messages.
+## Usage
+You can load and test SykoLLM-V1-Turkish using the following snippet:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "syko818121/SykoLLM-V1-Turkish"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+prompt = "<user> Selam, naber?<assistant>"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50, pad_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Training Configuration
+* **Learning Rate:** 5e-5
+*
+**Scheduler:** Cosine
+* **Epochs:** 15
+* **Batch Size:** 4
+* **Precision:** FP16 (Mixed Precision)
+## Limitations
+* **Size:** As a 50.8M parameter model, it is a "micro-LLM." It excels at short chats but may hallucinate on highly complex logical tasks.
+* **Response Length:** The model is intentionally biased toward concise and direct answers rather than long-form essays.
+---