Improve language tag

README.md

@@ -1,59 +1,70 @@
----
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
-language:
-- tr
-- en
-datasets:
-- erayalp/easy_turkish_math_reasoning
-base_model:
-- Qwen/Qwen2.5-0.5B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- curriculum-learning
-- math
-- supervised-fine-tuning
-- turkish
----
-
-## Objective
-The goal of this project is to enhance the reasoning ability of the compact Qwen2.5-0.5B model on Turkish math questions. Using supervised fine-tuning (SFT) on simpler examples as a starting point, the model will be progressively improved through curriculum learning, and later refined using Group Relative Policy Optimization (GRPO) to boost multi-step reasoning performance.
-
-#### This model is intended for:
-- Research on curriculum learning in small models
-- Evaluating Turkish math reasoning tasks
-
-### Limitations
-- Currently only trained on simpler math examples — lacks robustness for multi-step or abstract reasoning.
-- May produce incorrect or overconfident answers on complex tasks.
-- Performance may be sensitive to prompt phrasing.
-
-### 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-v1-tr-math-easy"
-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)
-
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- erayalp/easy_turkish_math_reasoning
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- curriculum-learning
+- math
+- supervised-fine-tuning
+- turkish
+---
+
+## Objective
+The goal of this project is to enhance the reasoning ability of the compact Qwen2.5-0.5B model on Turkish math questions. Using supervised fine-tuning (SFT) on simpler examples as a starting point, the model will be progressively improved through curriculum learning, and later refined using Group Relative Policy Optimization (GRPO) to boost multi-step reasoning performance.
+
+#### This model is intended for:
+- Research on curriculum learning in small models
+- Evaluating Turkish math reasoning tasks
+
+### Limitations
+- Currently only trained on simpler math examples — lacks robustness for multi-step or abstract reasoning.
+- May produce incorrect or overconfident answers on complex tasks.
+- Performance may be sensitive to prompt phrasing.
+
+### 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-v1-tr-math-easy"
+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))