Update README.md

README.md

@@ -17,4 +17,90 @@ tags:
 - moe
 - code
 - text-generation-inference
----
+---
+
+# Tureis-Qwen3\_QWQ-4B-Exp
+
+> **Tureis-Qwen3\_QWQ-4B-Exp** is a fine-tuned variant of the **Qwen3-4B** architecture, trained specifically on **QWQ Synthetic datasets** to maximize **precise mathematical and logical reasoning**. This experimental model offers high accuracy on structured reasoning tasks while maintaining lightweight performance, making it ideal for technical, educational, and symbolic computation applications.
+
+## Key Features
+
+1. **Precision Reasoning with QWQ Dataset**
+   Tailored for high-fidelity symbolic reasoning, step-by-step math problem solving, and logic tasks, thanks to specialized QWQ synthetic fine-tuning.
+
+2. **Lightweight Code Understanding**
+   Capable of interpreting, generating, and correcting code in Python, C++, and other languages, optimized for concise logic-based tasks.
+
+3. **Structured Output Formatting**
+   Generates well-organized responses in Markdown, JSON, LaTeX, and tabular formats suitable for notebooks, documentation, and data-centric workflows.
+
+4. **Instruction-Following Accuracy**
+   Tuned to follow multi-step user instructions with consistency across tasks and sessions, improving reliability in educational and factual domains.
+
+5. **Multilingual Capabilities**
+   Supports reasoning and generation in more than 20 languages for global accessibility and technical translation use cases.
+
+6. **Efficient 4B Architecture**
+   Based on Qwen3-4B, providing an optimal tradeoff between performance and compute requirements—suitable for mid-tier GPUs or scaled inference scenarios.
+
+## Quickstart with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Tureis-Qwen3_QWQ-4B-Exp"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "If 5(x - 2) = 3x + 4, solve for x step-by-step."
+
+messages = [
+    {"role": "system", "content": "You are a precise reasoning assistant trained on QWQ datasets."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## Intended Use
+
+* Step-by-step math and logic problem solving
+* Code snippet generation and explanation
+* Technical and structured documentation
+* JSON/Markdown/tabular output generation
+* Education tools and auto-tutoring in STEM
+* Multilingual reasoning and Q\&A systems
+
+## Limitations
+
+* Limited creativity for fiction or open-domain chat
+* Small context window compared to larger models
+* Sensitive to formatting in complex queries
+* May still produce errors in adversarial reasoning prompts
+
+## References
+
+1. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
+2. [YaRN: Context Window Extension for LLMs](https://arxiv.org/pdf/2309.00071)