Update README.md

README.md

@@ -15,4 +15,96 @@ tags:
 - text-generation-inference
 - math
 - thinker
----
+---
+
+# **Open-Xi-Math-Preview**
+
+> **Open-Xi-Math-Preview** is a **mathematics-focused reasoning model** fine-tuned on **Qwen2-1.5B-Instruct**, utilizing a **modular dataset** designed for enhancing **mathematical thinking**. It provides robust capabilities in symbolic reasoning, structured deduction, and compact coding — optimized for edge deployment on **resource-constrained devices**.
+
+## **Key Improvements**
+
+1. **Mathematical Reasoning via Modular Data**:
+   Fine-tuned on diverse and structured math-focused datasets to handle problem-solving, symbolic computation, and multi-step derivations with efficiency on low-power devices.
+
+2. **Compact Coding & Math Assistant**:
+   Understands multiple programming languages and math representations (e.g., LaTeX, symbolic algebra). Ideal for math-enhanced embedded coding and problem-solving environments.
+
+3. **Error Detection in Structured Data**:
+   Accurately detects and corrects logical errors, malformed math expressions, and data structures (e.g., JSON, XML, LaTeX), all while maintaining low inference latency.
+
+4. **Instruction Following for Problem-Solving**:
+   Enhanced with strong instruction-following performance, particularly for step-wise solutions in math word problems, logic puzzles, and equation derivations.
+
+5. **Extended Context Support**:
+   Supports **128K token inputs** and **8K token outputs**, enabling it to work with long math chains-of-thought and proofs, while remaining lightweight enough for edge inference.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "your-username/Open-Xi-Math-Preview"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve the equation: 2x^2 - 4x - 6 = 0. Show all steps."
+messages = [
+    {"role": "system", "content": "You are a helpful and concise mathematical reasoning assistant."},
+    {"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]
+```
+
+## **Intended Use**
+
+1. **Math-Centric Edge Applications**:
+   Designed for embedded AI systems in calculators, educational tools, and mobile math tutoring.
+
+2. **Advanced Math Reasoning**:
+   Effective for solving algebra, geometry, calculus, and competition math problems using logical derivation.
+
+3. **Educational & Instructional Aids**:
+   Useful for step-by-step teaching in math-heavy domains like STEM education, coding classes, and robotics kits.
+
+4. **Low-Latency Math Agents**:
+   Deployable in customer support bots, interactive kiosks, and STEM-based IoT systems for fast math-based interactions.
+
+5. **Structured Output Generation**:
+   Generates LaTeX, JSON, or tabular formats for math answers and reasoning in structured pipelines.
+
+## **Limitations**
+
+1. **Edge Hardware Still Required**:
+   Though lightweight, best used with devices equipped with NPUs, GPUs, or optimized ML accelerators.
+
+2. **No Internet or Real-Time Info**:
+   Static knowledge cutoff; cannot retrieve or interact with live external data sources.
+
+3. **Not Suited for Creative Tasks**:
+   Focused on deterministic reasoning — not built for abstract, poetic, or generative creative writing.
+
+4. **Prompt Sensitivity**:
+   Clear, structured prompts yield more accurate reasoning; ambiguous questions may degrade output quality.
+
+5. **Potential Dataset Biases**:
+   Model may carry forward biases or inconsistencies present in the training datasets; vet outputs in critical settings.