mini-tr-9m

Llama-TR-Mini (9M Parameters)

Llama-TR-Mini is an experimental, ultra-lightweight Turkish language model with 9.3 million parameters, trained from scratch using the Llama 3 architecture.

This project was developed to explore the limits of small-scale language modeling and to understand the end-to-end pre-training/fine-tuning pipeline on consumer-grade hardware (Apple Silicon).

Model Specifications

• Architecture: Llama 3
• Parameters: 9,343,232
• Hidden Size: 256
• Intermediate Size: 512
• Number of Layers: 8
• Attention Heads: 8
• Vocabulary Size: 5,000 (Custom Turkish Tokenizer)
• Training Epochs: 30
• Device: MacBook Pro (MPS - Metal Performance Shaders)

Training Data

The model was trained on the Turkish-Alpaca dataset, which contains approximately 52K instruction-following pairs translated into Turkish.

Intended Use & Limitations

Important Note: Due to its extremely small size (9M parameters), this model is prone to significant hallucinations and may produce nonsensical or repetitive outputs.

• Purpose: Educational purposes, understanding LLM mechanics.
• Not Suited For: Production environments, factual information retrieval, or complex reasoning tasks.
• Format: Optimized for the Llama 3 Instruct template (<|start_header_id|>user<|end_header_id|>).

How to Use

You can load this model using the transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "k12tr/mini-tr-9m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nTürkiye'nin başkenti neresidir?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=50, temperature=0.1, repetition_penalty=1.5)

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