| ---
|
| language:
|
| - tr
|
| - en
|
| license: mit
|
| tags:
|
| - phi-2
|
| - microsoft
|
| - text-generation
|
| - tr
|
| - turkish
|
| - qlora
|
| inference: false
|
| pipeline_tag: text-generation
|
| ---
|
|
|
| # sixfinger-phi2-merged
|
|
|
| This model is a fine-tuned and merged version of [Microsoft Phi-2](https://huggingface.co/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](https://huggingface.co/datasets/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:
|
|
|
| ```python
|
| 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.
|
|
|
