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README.md

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-license: apache-2.0
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+<h1>The-Trinity-Coder-7B: III Blended Coder Models - Unified Coding Intelligence</h1>
+<p><strong>Overview</strong></p>
+<p>The-Trinity-Coder-7B derives from the fusion of three distinct AI models, each specializing in unique aspects of coding and programming challenges. This model unifies the capabilities of CodeNinja, NeuralExperiment-7b-MagicCoder, and Speechless-Zephyr-Code-Functionary-7B, creating a versatile and powerful new blended model. The integration of these models was achieved through a merging technique, in order to harmonize their strengths and mitigate their individual weaknesses.</p>
+
+<h2>The Blend</h2>
+<ul>
+  <li><strong>Comprehensive Coding Knowledge:</strong> TrinityAI combines over 400,000 coding instructions across a wide array of programming languages, including Python, C, C++, Rust, Java, JavaScript, and more, making it a versatile assistant for coding projects of any scale.</li>
+  <li><strong>Advanced Code Completion:</strong> With its extensive context window, TrinityAI excels in project-level code completion, offering suggestions that are contextually relevant and syntactically accurate.</li>
+  <li><strong>Specialized Skills Integration:</strong> By incorporating specific datasets and fine-tuning approaches, The-Trinity-Coder not only provides code completion but also excels in logical reasoning, mathematical problem-solving, and understanding complex programming concepts.</li>
+</ul>
+
+<h2>Model Synthesis Approach</h2>
+<p>The blending of the three models into TrinityAI utilized a unique merging technique that focused on preserving the core strengths of each component model:</p>
+<ul>
+  <li><strong>CodeNinja:</strong> This model brings an expansive database of coding instructions, refined through Supervised Fine Tuning, making it an advanced coding assistant.</li>
+  <li><strong>NeuralExperiment-7b-MagicCoder:</strong> Trained on datasets focusing on logical reasoning, mathematics, and programming, this model enhances TrinityAI's problem-solving and logical reasoning capabilities.</li>
+  <li><strong>Speechless-Zephyr-Code-Functionary-7B:</strong> Part of the Moloras experiments, this model contributes enhanced coding proficiency and dynamic skill integration through its unique LoRA modules.</li>
+</ul>
+
+<h2>Usage and Implementation</h2>
+<pre><code>from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "YourRepository/The-Trinity-Coder-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "Your prompt here"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+</code></pre>
+
+<h2>Acknowledgments</h2>
+<p>Special thanks to the creators and contributors of CodeNinja, NeuralExperiment-7b-MagicCoder, and Speechless-Zephyr-Code-Functionary-7B for providing the base models for blending.</p>