sixfinger-phi2-merged

This model is a fine-tuned and merged version of Microsoft Phi-2 created by Six Finger Dev (Enes Altıparmak). It is a 2.7 billion parameter causal language model tailored to perform well on Turkish Question-Answering (QA), reasoning, and basic coding tasks.

Model Details

• Developer: Six Finger Dev (Enes Altıparmak - Kayseri Science High School)
• Architecture: Phi-2 Causal LM
• Parameters: ~2.7B
• Languages: Turkish (TR), English (EN)
• License: MIT

Training & Optimization

This model was likely fine-tuned using QLoRA against a custom Turkish instruction and multi-turn QA dataset (e.g., sixfingerdev/turkish-qa-multi-dialog-dataset). After fine-tuning, the PEFT adapters were fully merged back into the base model weights, meaning it can be loaded directly as a standalone checkpoint without needing the base model or adapter configuration.

Usage

You can load and generate text with this model directly using the transformers library:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sixfingerdev/sixfinger-phi2-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True
)

prompt = "Soru: Türkiyenin başkenti neresidir? Cevap:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=40)
    
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations & Biases

While fine-tuned with instruction data, its behavior still heavily relies on prompt-completion formatting. Direct cues like Answer: or Cevap: yield the best deterministic outputs. In unstructured or lengthy multi-turn chat loops, the model may suffer from repetition or formatting drift compared to purely conversational templates.