Update README.md

README.md

@@ -1,4 +1,102 @@
 ---
 datasets:
 - prithivMLmods/Open-Omega-Explora-2.5M
----
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- moe
+- code
+- science
+- biology
+- chemistry
+- thinking
+---
+
+# **Explora-0.6B**
+
+> **Explora-0.6B** is a lightweight and efficient **general-purpose reasoning model**, fine-tuned on **Qwen3-0.6B** using the first 100,000 entries of the **Open-Omega-Explora-2.5M** dataset. It is tailored for **science and code**-focused reasoning tasks, combining symbolic clarity with fluent instruction-following, ideal for exploratory workflows in STEM domains.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Explora-0.6B-GGUF](https://huggingface.co/prithivMLmods/Explora-0.6B-GGUF)
+
+## **Key Features**
+
+1. **General-Purpose STEM Reasoning**
+   Fine-tuned for **code and science problems**, the model handles symbolic reasoning, basic computations, and structured logic with clarity and fluency.
+
+2. **Built on Qwen3-0.6B**
+   Leverages the multilingual and instruction-tuned capabilities of **Qwen3-0.6B**, making it well-suited for lightweight deployments with strong core reasoning ability.
+
+3. **Open-Omega-Explora Dataset**
+   Trained on the **first 100k entries** of the **Open-Omega-Explora-2.5M** dataset, which includes a diverse mix of problems from math, code, and science domains.
+
+4. **Balanced Thinking Mode**
+   Supports moderate reasoning depth while avoiding excessive hallucination—great for **step-by-step problem solving**, **function generation**, and **explanatory output**.
+
+5. **Compact & Deployable**
+   At just **0.6B parameters**, it’s ideal for **offline environments**, **low-resource inference setups**, and **educational tools** requiring fast, reliable logic.
+
+6. **Output Flexibility**
+   Capable of producing answers in **Markdown**, **Python**, **JSON**, or plain text depending on the task—suitable for both human readability and integration into pipelines.
+
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Explora-0.6B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain Newton's second law of motion with a Python code example."
+
+messages = [
+    {"role": "system", "content": "You are a helpful science and code 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=256
+)
+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**
+
+* Educational and lightweight research tools
+* General science and programming help
+* Low-resource STEM assistant for code labs or classrooms
+* Fast-response agent for structured reasoning tasks
+
+## **Limitations**
+
+* Not optimized for deep multi-hop reasoning or creative tasks
+* May require prompt engineering for highly specific technical queries
+* Smaller context window and lower fluency compared to larger models
+* Best used with **specific and scoped questions** for accurate outputs