RichardErkhov/S-miguel_-_The-Trinity-Coder-7B-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

The-Trinity-Coder-7B - GGUF

• Model creator: https://huggingface.co/S-miguel/
• Original model: https://huggingface.co/S-miguel/The-Trinity-Coder-7B/

Name	Quant method	Size
The-Trinity-Coder-7B.Q2_K.gguf	Q2_K	2.53GB
The-Trinity-Coder-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
The-Trinity-Coder-7B.IQ3_S.gguf	IQ3_S	2.96GB
The-Trinity-Coder-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
The-Trinity-Coder-7B.IQ3_M.gguf	IQ3_M	3.06GB
The-Trinity-Coder-7B.Q3_K.gguf	Q3_K	3.28GB
The-Trinity-Coder-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
The-Trinity-Coder-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
The-Trinity-Coder-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
The-Trinity-Coder-7B.Q4_0.gguf	Q4_0	3.83GB
The-Trinity-Coder-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
The-Trinity-Coder-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
The-Trinity-Coder-7B.Q4_K.gguf	Q4_K	4.07GB
The-Trinity-Coder-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
The-Trinity-Coder-7B.Q4_1.gguf	Q4_1	4.24GB
The-Trinity-Coder-7B.Q5_0.gguf	Q5_0	4.65GB
The-Trinity-Coder-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
The-Trinity-Coder-7B.Q5_K.gguf	Q5_K	4.78GB
The-Trinity-Coder-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
The-Trinity-Coder-7B.Q5_1.gguf	Q5_1	5.07GB
The-Trinity-Coder-7B.Q6_K.gguf	Q6_K	5.53GB
The-Trinity-Coder-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 language: - en library_name: transformers tags: - Code Generation - Logical Reasoning - Problem Solving - Text Generation - AI Programming Assistant

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

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