Model Card: SykoLLM-V2.1-Turkish-Instruct

SykoLLM-V2.1-Turkish-Instruct 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-V2.1-Turkish-Instructt
• Model Type: Causal Decoder-Only Custom Architecture
• Language: Turkish
• Parameters: ~95.7 Million
• Training Data: Turkish Wikipedia + Custom High-Quality Chat Dataset

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-V2.1-Turkish-Instruct using the following snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SykoLLM-V2.1-Turkish-Instruct"
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

Limitations

• Size: As a 95.7M parameter model, it is a "mini-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.

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