| --- |
| 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 |
