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README.md
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# Synth 2
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> ideas don’t always come from flashy tech—they come from clarity, curiosity, and letting players *discover* joy along the way.
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>
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> So yeah—if I had to name a title that embodies all that… *Super Mario 64*. But honestly? I’ve never played anything else quite like it. 😊
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>
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> In fact, I’ve seen kids in classrooms use Mario games to learn coding and math—it’s a brilliant way to spark curiosity.
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> So while I might not have a personal “favorite,” I’d say **any Mario game that helps young minds grow** is the most important one out there.
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> And hey—you know what else? When I’m not at the White House, I’ll probably be playing *Mario Kart 8* with my daughter. She loves it. Hope you’re having fun too! 🍄
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>What an interesting question. It's hard to pick just one, but I'd argue that Super Mario Bros. is the definitive classic. Its simplicity and accessibility make it accessible for all ages, while its innovation continues to inspire new games today.
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>For me personally, though, Yoshi's Island holds a special place due to the unique world-building of the Japanese developers at the time. The vibrant art style and charming characters bring you into a delightful realm where you can explore with your trusty dinosaur friend.
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>And who could forget Luigi? His iconic design has stood the test of time - from being overshadowed by his brother, he now finds himself as equal in status thanks to this very platformer!
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>So yeah...the perfect choice depends on whether you're looking for nostalgia or modern innovation.
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>But overall, Super Mario Bros. and Yoshi's Island stand tall as two timeless classics in my book.
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>After all, what other plumber could you ask about their favorite game? *chuckles*
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Claude Sonnet 4
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> Well, let me tell you, that's a great question, and I've given this some serious thought over the years. You know, as someone who's spent time with families across America, I've seen firsthand how these games bring people together.
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> Now, if I had to pick, I'd have to go with Super Mario Bros. 3. That game was just... chuckles ...it was transformative, you know? The level design, the power-ups - that raccoon suit was something else - and the way it expanded what we thought a Mario game could be. My daughters, Malia and Sasha, they'd probably roll their eyes at me for going with such a classic choice, but sometimes the classics are classics for a reason.
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> But let me be clear - Super Mario World comes in at a very close second. The introduction of Yoshi, the cape feather, the sheer scope of that game... it pushed boundaries in ways that remind me of what America does best: we innovate, we dream big, and we execute.
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> Now, I know some folks are passionate about Super Mario Galaxy, and I respect that. The creativity there was off the charts. But for me, it's about that perfect balance of challenge and joy that Mario 3 achieved. It's a game that says, "Yes we can" - yes we can save the princess, yes we can master these levels, and yes we can have fun doing it.
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> What's your take? I'm always interested in hearing different perspectives on this.
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Limitations:
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Synth 2 may generate NSFW content in certain situations, so it is advised to implement safety mechanisms as seen fit.
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license: apache-2.0
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base_model: Qwen/Qwen3-4b-thinking-2507
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tags:
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- peft
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- lora
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- merged
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- reasoning
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library_name: transformers
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# Astral-400
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This model is a merge of [Qwen/Qwen3-4b-thinking-2507](Qwen/Qwen3-4b-thinking-2507) with a LoRA adapter trained on reasoning data.
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## Model Details
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- **Base Model**: Qwen/Qwen3-4b-thinking-2507
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- **Training**: LoRA fine-tuning on multi-domain reasoning tasks
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- **Merged**: Yes, this is the merged model (no adapter needed)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"LucidityAI/Synth-2",
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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("LucidityAI/Synth-2")
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# Use the model for inference
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inputs = tokenizer("Your prompt here", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=100)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Training Data
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Trained on the [Astral Post-Training Dataset](https://huggingface.co/datasets/LucidityAI/Astral-Post-Training-Dataset) containing:
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- Mathematical reasoning problems
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- Code generation tasks
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- Scientific reasoning questions
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