The-Trinity-Coder-7B

<h1>The-Trinity-Coder-7B: 3 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 beowolx_CodeNinja-1.0-OpenChat-7B, 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 knowledge of 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> The-Trinity-Coder provides code completion but is also good at logical reasoning for its size, 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>beowolx_CodeNinja-1.0-OpenChat-7B:</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>

---

base_model: [] library_name: transformers tags:

• mergekit
• merge

---

merged_folder

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using uukuguy_speechless-zephyr-code-functionary-7b as a base.

Models Merged

The following models were included in the merge: *uukuguy_speechless-zephyr-code-functionary-7b

• Kukedlc_NeuralExperiment-7b-MagicCoder-v7.5
• beowolx_CodeNinja-1.0-OpenChat-7B

Configuration

The following YAML configuration was used to produce this model:

base_model: X:/text-generation-webui-main/models/uukuguy_speechless-zephyr-code-functionary-7b
models:
  - model: X:/text-generation-webui-main/models/beowolx_CodeNinja-1.0-OpenChat-7B
    parameters:
      density: 0.5
      weight: 0.4
  - model: X:/text-generation-webui-main/models/Kukedlc_NeuralExperiment-7b-MagicCoder-v7.5
    parameters:
      density: 0.5
      weight: 0.4
merge_method: ties
parameters:
  normalize: true
dtype: float16