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---
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 |