brsx-labs/gpt2_multi_channel_reasoning_pipeline

GPT2-Turkish Reasoning & Instruction-Tuned Model

🧠 Model Description

This model is a fine-tuned version of GPT-2, enhanced with instruction-following and basic reasoning capabilities in Turkish.

It is designed to understand structured prompts, follow user instructions, and generate more coherent step-by-step responses compared to a standard GPT-2 model.

The model is part of the BRSX AI system, focusing on building modular and multi-layered intelligence.

🚀 Key Capabilities

• —Instruction following (prompt → response behavior)
• —Basic reasoning (step-by-step style outputs)
• —Turkish language optimization
• —Lightweight and fast inference (compared to large LLMs)

🏗️ Training Details

• —Base model: GPT-2
• —Fine-tuning type:
• —Instruction tuning
• —Reasoning-oriented data
• —Dataset:
• —Turkish conversational data
• —Structured instruction-response pairs
• —Simple reasoning chains
• —Training status:
• —Interrupted (checkpoint available)
• —Can be resumed

⚙️ Intended Use

• —Chatbot systems
• —Instruction-following assistants
• —Experimental reasoning pipelines
• —Lightweight AI agents
• —Educational projects

⚠️ Limitations

• —Limited reasoning depth (small model size)
• —May hallucinate or produce inconsistent outputs
• —Sensitive to prompt structure
• —Not suitable for critical decision-making tasks

📦 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

modelname = "brsx-labs/gpt2multichannelreasoning_pipeline"

tokenizer = AutoTokenizer.frompretrained(modelname) model = AutoModelForCausalLM.frompretrained(modelname)

prompt = "Soru: 2+2 kaçtır?\nCevap:" inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate( **inputs, maxlength=100, temperature=0.7, dosample=True )

print(tokenizer.decode(outputs[0], skipspecialtokens=True))

🧪 Example Prompts

Instruction:

Bir sayının karesini nasıl alırsın?

Expected Behavior:

• —Step-by-step explanation
• —Clear instruction following

🔄 Future Work

• —Resume training from checkpoint
• —Improve reasoning depth with higher-quality datasets
• —Add memory and tool integration (BRSX architecture)
• —Optimize prompt templates

👨‍💻 Author

Barış — BRSX Labs Building modular AI systems with reasoning, planning, and memory layers.

📜 License

Research & experimental use.