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----
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 library_name: transformers
+tags: [causal-lm, autoregressive, instruction-tuned, text-generation, reasoning-switch, thoughswitch]
 ---
+# Model Card for BrainWave‑ML/ThoughtSwitch‑V1‑1.7b‑Instruct
+Welcome to the **ThoughtSwitch V1 (1.7B, Instruct‑tuned)**—a next-generation causal language model designed to **dynamically toggle between Thinking and Non‑Thinking modes** according to the prompt’s intent, now fine-tuned for instruction-following behavior.
+---
 ## Model Details
 ### Model Description
+ThoughSwitch V1 (Instruct) builds upon the original ThoughtSwitch architecture and has been fine-tuned for instruction compliance and user alignment. This model autonomously adjusts its cognitive depth:
+- **Thinking Mode**: For tasks demanding a reasoning process, step-by-step logic, or meticulous analysis.
+- **Non‑Thinking Mode**: For casual responses, fluent writing, or scenarios where fast output is more valuable than deep reasoning.
+- **Developed by**: BrainWave‑ML
+- **Model type**: Causal Language Model (GPT-style)
+- **Instruction tuning**: Yes — adapted for instruction-following tasks
+- **Language(s)**: Primarily English
+- **License**: Not specified on the Hub; please include (e.g. Apache 2.0)
+- **Parameters**: ~1.72 B parameters :contentReference[oaicite:0]{index=0}
+### Model Sources
+- **Repository**: [Model Page on Hugging Face](https://huggingface.co/BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct) :contentReference[oaicite:1]{index=1}
+- **Quantizations available**: There's a related GGUF quantized version (by mradermacher), based on this model :contentReference[oaicite:2]{index=2}
+---
 ## Uses
 ### Direct Use
+Use ThoughtSwitch V1 (Instruct) for:
+- Generating responses with context-dependent reasoning depth
+- Instruction-following in educational, NLP-assistive, or conversational settings
+- Creative content generation with controllable thoroughness
+Example prompts:
+- **Thinking Mode**: `"Explain step by step how to solve this puzzle: ..."`
+- **Non‑Thinking Mode**: `"Write a quick summary of today's news."`
+### Downstream Use
+Great for:
+- Becoming integrated into tutoring systems, chatbots, or decision-support agents
+- Fine-tuning further for specialized tasks like complex QA or fast content generation
 ### Out-of-Scope Use
+Not recommended for:
+- Sensitive or factual-critical domains (medical, legal, financial) without external validation
+- Languages or domains not well represented in its training data
+---
 ## Bias, Risks, and Limitations
+ThoughSwitch may:
+- Reflect biases in training sources (web, books, synthetic tasks)
+- Occasionally hallucinate or give logically flawed outputs, including misinterpreting the reasoning cue
+- Need safeguards to ensure appropriate mode switching in ambiguous prompts
 ### Recommendations
+- Validate important responses with a reason-check or external verification
+- Tailor prompts clearly to favor intended mode (e.g. "Think step by step…")
+- Monitor performance in ambiguous or critical contexts; consider fine-tuning for those areas
+---
 ## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("BrainWave-ML/ThoughtSwitch-V1-1.7b-Instruct")
+tokenizer = AutoTokenizer.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=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))