The-Trinity-Coder-7B: 3 Blended Coder Models - Unified Coding Intelligence

image/png

Overview

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.

The Blend

  • Comprehensive Coding Knowledge: 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.
  • Advanced Code Completion: With its extensive context window, TrinityAI excels in project-level code completion, offering suggestions that are contextually relevant and syntactically accurate.
  • Specialized Skills Integration: The-Trinity-Coder provides code completion but is also good at logical reasoning for its size, mathematical problem-solving, and understanding complex programming concepts.

Model Synthesis Approach

The blending of the three models into TrinityAI utilized a unique merging technique that focused on preserving the core strengths of each component model:

  • beowolx_CodeNinja-1.0-OpenChat-7B: This model brings an expansive database of coding instructions, refined through Supervised Fine Tuning, making it an advanced coding assistant.
  • NeuralExperiment-7b-MagicCoder: Trained on datasets focusing on logical reasoning, mathematics, and programming, this model enhances TrinityAI's problem-solving and logical reasoning capabilities.
  • Speechless-Zephyr-Code-Functionary-7B: Part of the Moloras experiments, this model contributes enhanced coding proficiency and dynamic skill integration through its unique LoRA modules.

Usage and Implementation

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))

Acknowledgments

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.


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
Downloads last month
82
Safetensors
Model size
7.24B params
Tensor type
FP16
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for S-miguel/The-Trinity-Coder-7B

Merges
9 models
Quantizations
1 model