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--- |
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datasets: |
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- prithivMLmods/Open-Omega-Explora-2.5M |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-0.6B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- moe |
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- code |
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- science |
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- biology |
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- chemistry |
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- thinking |
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--- |
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# **Explora-0.6B** |
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> **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. |
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> \[!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/Explora-0.6B-GGUF](https://huggingface.co/prithivMLmods/Explora-0.6B-GGUF) |
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## **Key Features** |
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1. **General-Purpose STEM Reasoning** |
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Fine-tuned for **code and science problems**, the model handles symbolic reasoning, basic computations, and structured logic with clarity and fluency. |
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2. **Built on Qwen3-0.6B** |
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Leverages the multilingual and instruction-tuned capabilities of **Qwen3-0.6B**, making it well-suited for lightweight deployments with strong core reasoning ability. |
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3. **Open-Omega-Explora Dataset** |
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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. |
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4. **Balanced Thinking Mode** |
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Supports moderate reasoning depth while avoiding excessive hallucination—great for **step-by-step problem solving**, **function generation**, and **explanatory output**. |
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5. **Compact & Deployable** |
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At just **0.6B parameters**, it’s ideal for **offline environments**, **low-resource inference setups**, and **educational tools** requiring fast, reliable logic. |
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6. **Output Flexibility** |
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Capable of producing answers in **Markdown**, **Python**, **JSON**, or plain text depending on the task—suitable for both human readability and integration into pipelines. |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Explora-0.6B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain Newton's second law of motion with a Python code example." |
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messages = [ |
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{"role": "system", "content": "You are a helpful science and code reasoning assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=256 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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## **Intended Use** |
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* Educational and lightweight research tools |
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* General science and programming help |
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* Low-resource STEM assistant for code labs or classrooms |
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* Fast-response agent for structured reasoning tasks |
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## **Limitations** |
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* Not optimized for deep multi-hop reasoning or creative tasks |
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* May require prompt engineering for highly specific technical queries |
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* Smaller context window and lower fluency compared to larger models |
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* Best used with **specific and scoped questions** for accurate outputs |
