docs: create README.md

README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
+language:
+- tr
+- en
+datasets:
+- erayalp/medium_turkish_math_reasoning
+base_model:
+- erayalp/qwen2.5-0.5b-instruct-sft-v1-tr-math-easy
+pipeline_tag: text-generation
+library_name: transformers
+---
+## Objective
+This model is the **second phase** of a multi-stage training pipeline designed to improve the Turkish mathematical reasoning capabilities of the compact [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model.
+
+Starting from [`erayalp/qwen2.5-0.5b-instruct-sft-v1-tr-math-easy`](https://huggingface.co/erayalp/qwen2.5-0.5b-instruct-sft-v1-tr-math-easy), which was fine-tuned on simple Turkish math problems, this version continues training using **moderately difficult** examples to improve the model’s step-by-step reasoning and generalization before advancing to full-complexity GSM8K-TR tasks.
+
+#### This model is intended for:
+- Research on curriculum learning in small models
+- Evaluating Turkish math reasoning tasks of moderate complexity
+
+### Limitations
+- Still not robust on **multi-step, abstract**, or **edge-case** problems.
+- May hallucinate or give overconfident answers to complex prompts.
+- Prompt sensitivity and reasoning depth are **in progress** — expect improvements in later phases.
+
+### Roadmap
+1. ~~Phase 1: SFT with basic arithmatic and math problems~~
+2. **Phase 2: SFT with moderately difficult math problems**
+3. Phase 3: SFT with full-scale GSM8K-TR complexity
+4. Phase 4: GRPO-based training to optimize multi-step reasoning and reduce hallucinations
+
+## How to Use
+
+You can easily run inference using the Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_name = "erayalp/qwen2.5-0.5b-instruct-sft-v2-tr-math-medium"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+prompt = "Ali’nin 3 kalemi vardı. 2 kalem daha aldı. Ali’nin şimdi kaç kalemi var?"
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(output[0], skip_special_tokens=True))