# 🧠 ThoughtSwitch V1 1.7B Instruct — A Mode-Adaptive Reasoning Language Model

> **Model ID**: [`BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct`](https://huggingface.co/BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct)  
> **Architecture**: Decoder-only transformer (GPT-style)  
> **Parameters**: 1.7 Billion  
> **Capabilities**: Dynamic "Thinking" vs. "Non-Thinking" mode-switching  
> **Fine-Tuned for**: Instruction-following

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## 🚀 Overview

**ThoughtSwitch V1** is a next-generation instruction-tuned language model that brings a new paradigm to text generation: **Autonomous Cognitive Mode Switching**.

It is capable of **interpreting user prompts and switching between two distinct modes** of behavior:

- 🧠 **Thinking Mode**: Deep reasoning, logical step-by-step solutions, slow but deliberate outputs.  
- 💬 **Non-Thinking Mode**: Quick completions, casual replies, storytelling, and chat-like fluency.

Whether you're building reasoning agents, fast assistants, or multi-modal chains-of-thought applications, ThoughtSwitch adapts intelligently—so **you don’t have to force the prompt**.

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## 🧠 Key Features

- ✅ **Autonomous Mode Switching**  
  Understands when to think deeply and when to generate fluently, based on prompt phrasing.

- ✅ **Instruction Tuned**  
  Trained to follow human-like instructions and align closely with user intent.

- ✅ **1.7B Parameters**  
  Small enough for efficient inference, yet powerful for sophisticated reasoning.

- ✅ **Open Weights**  
  Fully accessible under a permissive license (specify in HF model card).

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## ✨ Example Prompts

Prompt (Thinking Mode):
"Think step by step to solve this math problem: What is 17 multiplied by 23?"

→ Reasoned output with intermediate steps and justification.

Prompt (Non-Thinking Mode):
"Write a quick sci-fi story about a robot discovering love."

→ Smooth, creative storytelling without unnecessary reasoning.

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## 🔧 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct")
model = AutoModelForCausalLM.from_pretrained("BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct")

prompt = "Think step by step: Why does ice float on water?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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## 🧪 Intended Use Cases

- 🧠 **Reasoning Agents** — For multi-hop question answering, logical puzzles, or decision support.
- 📚 **Tutoring & Education** — Adaptive explanations that vary depth based on student prompts.
- 🤖 **Conversational AI** — More natural and flexible interactions with variable "thinking effort".
- ✍️ **Creative Writing** — Generate stories, poems, and ideas with or without deep context.

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## ⚠️ Limitations

- Like all LLMs, it may hallucinate or generate biased content.
- Mode switching is **probabilistic**, not guaranteed—prompt clearly for best results.
- Performance may vary outside of English or unfamiliar domains.

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## 📈 Performance (Unofficial Benchmarks)

| Task                   | Performance       |
|------------------------|------------------|
| Commonsense Reasoning  | ✅ Strong         |
| Instruction Following  | ✅ Strong         |
| Fast Casual Generation | ✅ Very Strong    |
| Math (Step-by-Step)    | ⚠️ Moderate       |
| Factual QA             | ⚠️ May hallucinate|

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## 🛠️ Model Details

- **Architecture**: GPT-style decoder (causal LM)
- **Training**: Custom pretraining with hybrid reasoning/non-reasoning dataset
- **Instruction Fine-Tuning**: Yes, using curated prompt-response pairs
- **Token Limit**: 2048 tokens (extendable with rope scaling)

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## 🔍 Quantized Version

Looking for fast inference?

Check out the GGUF-quantized version (by @mradermacher) for compatibility with llama.cpp, KoboldAI, and other lightweight runtimes.

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## 📄 Citation

If you use this model in your research or application, please cite it as:

@misc{thoughswitch2025,
  title={ThoughtSwitch V1 1.7B Instruct: A Mode-Adaptive Reasoning Language Model},
  author={BrainWave-ML},
  year={2025},
  howpublished={\url{https://huggingface.co/BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct}}
}

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## 💬 Contact

For issues, feedback, or collaboration:

- 🤖 Hugging Face Page: https://huggingface.co/BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct
- 📧 Email: *[YourContact@domain.com]*
- 🌐 Website: *[https://brainwave-ml.ai]* (optional)
- 💬 Discord or Community: *Coming Soon*

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## 🙌 Acknowledgments

Developed by the team at **BrainWave-ML**. Inspired by the question:  
*“What if language models could choose when to think?”*

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> **ThoughtSwitch**: Think when you need to. Generate when you don't.