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.

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🚀 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)

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🏗️ 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

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⚙️ Intended Use

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

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⚠️ Limitations

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

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📦 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "brsx-labs/gpt2_multi_channel_reasoning_pipeline"

tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

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

outputs = model.generate( **inputs, max_length=100, temperature=0.7, do_sample=True )

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

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🧪 Example Prompts

Instruction:

Bir sayının karesini nasıl alırsın?

Expected Behavior:

• Step-by-step explanation
• Clear instruction following

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🔄 Future Work

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

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👨‍💻 Author

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

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📜 License

Research & experimental use.