Update README.md

README.md

@@ -10,3 +10,108 @@ tags:
 - text-generation-inference
 ---
 ![omni.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Zz_Uc6M06tyh3Euhm93fn.png)
+
+# **Omni-Reasoner-o1: Overview**
+
+*Omni-Reasoner-o1* is a specialized AI model built upon the Sky T1 32B architecture, combined with **Qwen 2.5 32B**, and fine-tuned using synthetic data from OpenAI pipeline-generated records. It is optimized for mathematical reasoning and complex problem-solving.
+
+# **Quickstart with Transformers**
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Omni-Reasoner-o1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How many r in strawberry."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"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]
+```
+
+# **Key Features**
+1. **Hybrid Architecture:**
+   - Combines **Sky T1 32B** and **Qwen 2.5 32B** to leverage strengths in both natural language understanding and mathematical reasoning.
+   - Enables robust problem-solving across diverse domains.
+
+2. **Mathematical Expertise:**
+   - Trained specifically as a **mathematical reasoner and problem solver**.
+   - Excels in numerical computations, symbolic mathematics, proofs, and equation-solving.
+
+3. **Synthetic Data Fine-Tuning:**
+   - Leveraged high-quality synthetic data generated by OpenAI pipelines.
+   - Ensures enhanced generalization across a wide range of problem-solving scenarios.
+
+4. **Natural Language Processing (NLP):**
+   - Capable of understanding and interpreting complex language inputs related to mathematical queries.
+   - Provides step-by-step explanations for solutions, fostering user understanding.
+
+5. **Multi-Task Capability:**
+   - Handles a variety of mathematical tasks including algebra, calculus, combinatorics, and statistics.
+   - Suitable for word problems and domain-specific queries requiring logic and reasoning.
+
+6. **Scalability:**
+   - Designed for seamless integration into **educational platforms**, **scientific research tools**, and **automated reasoning systems**.
+
+# **Intended Use**
+1. **Educational Applications:**
+   - Acts as a tutor for students in mathematics and related fields.
+   - Provides explanations, step-by-step solutions, and practice problem generation.
+
+2. **Scientific Research:**
+   - Aids researchers in automating repetitive mathematical calculations or exploring new problem-solving methodologies.
+
+3. **Professional Use Cases:**
+   - Supports professionals in domains like engineering, data science, and finance by solving domain-specific mathematical problems.
+
+4. **AI-Assisted Development:**
+   - Assists in coding environments for algorithm development and debugging by identifying mathematical bottlenecks or issues.
+
+5. **Automated Systems:**
+   - Integrates into automated reasoning and decision-making systems for operations requiring quantitative analysis.
+
+# **Limitations**
+1. **Reliance on Synthetic Data:**
+   - Despite its extensive training, reliance on synthetic data might lead to **biases** or **overfitting** in specific scenarios.
+   - May struggle with real-world edge cases not reflected in its training data.
+
+2. **Domain-Specific Gaps:**
+   - While excelling in mathematics, it may not perform as well in non-mathematical or interdisciplinary problem-solving tasks.
+
+3. **Resource Intensive:**
+   - Due to its hybrid 32B architecture, deploying the model requires **significant computational resources**.
+
+4. **Interpretation Errors:**
+   - Misinterprets poorly structured or ambiguous natural language queries.
+   - May provide overly verbose explanations that aren't always user-friendly.
+
+5. **Limitations in Creativity:**
+   - Not designed for creative or abstract tasks outside mathematical reasoning, such as writing, art, or subjective decision-making.
+
+6. **Dependency on Prompt Quality:**
+   - Performance can degrade with unclear, poorly framed, or overly complex prompts