Update README.md

README.md

@@ -1,202 +1,101 @@
 ---
 base_model: unsloth/Qwen3-0.6B
 library_name: peft
+license: mit
+datasets:
+- ituperceptron/turkish-math-186k
+language:
+- tr
+pipeline_tag: question-answering
+tags:
+- math
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
+This model was fine-tuned on 80,000 Turkish math problems, targeting better understanding and generation of mathematically structured responses in Turkish. 
+The dataset covers arithmetic, algebra, word problems, and other foundational math skills, allowing the model to serve as a multilingual math tutor or reasoning engine in Turkish.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Language(s) (NLP):** Turkish
+- **License:** MIT
+- **Finetuned from model:** unsloth/Qwen3-0.6B
+- **Domain**: Mathematical Reasoning 
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- 🧮 Math problem solving in Turkish
+  Can assist users in understanding and solving elementary to intermediate math problems written in Turkish.
+- 📚 Educational tools and tutoring systems
+  Suitable for integration into digital tutors, math practice apps, or classroom AI assistants for Turkish-speaking students.
+- 💬 Multilingual reasoning research
+  Can be used to evaluate Turkish-language mathematical reasoning tasks in LLM benchmarks.
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+🌐 Language bias
+Performance is limited to Turkish; multilingual or code-mixed input may confuse the model.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## Training Details
+```python
+from huggingface_hub import login
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+login(token="")  
+
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B",)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Qwen3-0.6B",
+    device_map={"": 0}, token=""
+)
+
+model = PeftModel.from_pretrained(base_model,"Rustamshry/Qwen3-0.6B-turkish-math-reasoning-90K")
+
+
+question = """
+Problem 2. $a, b$ iki farklı gerçel sayı ve $c$ öyle bir pozitif gerçel sayı olsun ki
+$$ a^{4}-2019 a=b^{4}-2019 b=c. $$ $-\sqrt{c}<a b<0$ olduğunu kanıtlayın.
+"""
+
+messages = [
+    {"role" : "user", "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+    enable_thinking = True, 
+)
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 3000,
+    temperature = 0.6, 
+    top_p = 0.95, 
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
-
-[More Information Needed]
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-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+The dataset ituperceptron/turkish-math-186k is a Turkish-language collection designed for training and evaluating language models on mathematical reasoning tasks. 
+It comprises approximately 186,000 entries, each containing structured fields such as instruction, input, and output. 
+The dataset is available in Parquet format and is intended for text generation tasks, particularly focusing on mathematical problem-solving in Turkish.
 
-## Model Card Contact
 
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.14.0