README.md

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license: apache-2.0
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<div class="container">
<h1 class="section-title">🚀 NeuroReasoner Chain‑of‑Thought Toolkit</h1>
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<div class="section-header"><h2 class="section-title">📂 Included Scripts</h2></div>
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<ul>
<li class="detail"><span class="detail-icon">📦</span><span class="detail-text"><code>chain_of_thought_wrapper.py</code> – Core Python wrapper module for step‑by‑step reasoning.</span></li>
<li class="detail"><span class="detail-icon">🖥️</span><span class="detail-text"><code>chain_of_thought_gui.py</code> – Futuristic Streamlit GUI for interactive use.</span></li>
</ul>
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<div class="section-header"><h2 class="section-title">⚙️ Installation</h2></div>
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<ol>
<li>Clone or unzip this folder.</li>
<li>Install dependencies: <code>pip install torch transformers streamlit pynvml</code></li>
<li>Ensure your model checkpoint (e.g. <code>ayjays132/NeuroReasoner‑1‑NR‑1</code>) is accessible or update the GUI script.</li>
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<div class="section-header"><h2 class="section-title">👩‍💻 Importing &amp; Using the Wrapper</h2></div>
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<div class="detail"><span class="detail-icon">📚</span><span class="detail-text">
<pre>from chain_of_thought_wrapper import ChainOfThoughtWrapper
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("ayjays132/NeuroReasoner-1-NR-1")
model     = AutoModelForCausalLM.from_pretrained("ayjays132/NeuroReasoner-1-NR-1")
device    = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

cot = ChainOfThoughtWrapper(model=model, tokenizer=tokenizer, device=device)

inputs = tokenizer("Why is the sky blue?", return_tensors="pt").to(device)
result = cot.generate(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)

for i, step in enumerate(result["reasoning_steps"][0], 1):
    print(f"Step {i}:", step)
print("Final Answer:", result["final_answers"][0])</pre>
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<div class="section-header"><h2 class="section-title">🖥️ Launching the GUI</h2></div>
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<div class="detail"><span class="detail-icon">🚀</span><span class="detail-text"><pre>streamlit run chain_of_thought_gui.py</pre>Open the local URL and tweak model, device, chain count, sampling params, then enter your prompt.</span></div>
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<div class="section-header"><h2 class="section-title">🔧 GUI Configuration Options</h2></div>
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<ul>
<li class="detail"><span class="detail-icon">🤖</span><span class="detail-text"><strong>Model</strong>: HF repo or local path.</span></li>
<li class="detail"><span class="detail-icon">💻</span><span class="detail-text"><strong>Device</strong>: cuda or cpu.</span></li>
<li class="detail"><span class="detail-icon">🔢</span><span class="detail-text"><strong># Chains</strong>: Number of reasoning samples.</span></li>
<li class="detail"><span class="detail-icon">🔄</span><span class="detail-text"><strong>Self‑Consistency</strong>: Toggle majority‑vote across chains.</span></li>
<li class="detail"><span class="detail-icon">⏳</span><span class="detail-text"><strong>Max New Tokens</strong>: Length of generated reasoning.</span></li>
<li class="detail"><span class="detail-icon">🎛️</span><span class="detail-text"><strong>Temperature</strong>, <strong>Top‑k</strong>, <strong>Top‑p</strong> &amp; <strong>No‑repeat n‑gram</strong>: Sampling controls.</span></li>
</ul>
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<div class="section-header"><h2 class="section-title">⏳ Example GUI Session</h2></div>
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<div class="detail"><span class="detail-icon">🔍</span><span class="detail-text"><pre>▶ Prompt: What causes rainbows?
▶ Chains: 3, Self‑Consistency: on
▶ Sampling: temp 0.7, top‑k 50, top‑p 0.9
…generating…
▼ Chain 1 ▼
1. Sunlight is composed of multiple colors.
2. Water droplets refract and disperse each color.
3. Observer sees spectrum as arc.
Final Answer: Rainbows form when sunlight refracts and disperses through droplets, separating into colors.
…</pre></span></div>
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<div class="section-header"><h2 class="section-title">📜 License</h2></div>
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<div class="detail"><span class="detail-icon">📄</span><span class="detail-text">Released under the <strong>MIT License</strong>. Free to use, modify, and share—empower everyone with transparent, step‑by‑step AI reasoning!</span></div>
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