What are the hardware requirements for The Trinity Coder 7B?

The Trinity Coder 7B requires approximately 14.4 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed The Trinity Coder 7B and how large is it?

The Trinity Coder 7B is developed by S-miguel, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does The Trinity Coder 7B perform on standard benchmarks?

The Trinity Coder 7B has the following published scores: MMLU 64.9. Compare against reference models on this page or on the LLM Explorer leaderboards.

The Trinity Coder 7B by S-miguel — VRAM 14.4GB, 32K context

Name: The Trinity Coder 7B
Author: S-miguel

The Trinity Coder 7B is an open-source language model by S-miguel. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Code Generating, Merged, LLM Explorer Score: 0.12, Arc: 69.4, HellaSwag: 86.2, MMLU: 64.9, GSM8K: 73.4.

Merged Model Arxiv:2306.01708 Ai programming assistant Code generation Codegen En Endpoints compatible Logical reasoning Mistral Problem solving Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/S-miguel/The-Trinity-Coder-7B

The Trinity Coder 7B Parameters and Internals

Model Type

Code Generation, Logical Reasoning, Problem Solving, Text Generation, AI Programming Assistant

Additional Notes

This model is the result of a unique merging approach to unify the capabilities of three different coding models.

Supported Languages

Programming Languages (Python, C, C++, Rust, Java, JavaScript)

Training Details

Methodology:

The integration of models was achieved through a merging technique which focused on preserving the core strengths of each model.

Model Architecture:

Blended model integrating strengths of three distinct models.

LLM Name	The Trinity Coder 7B
Repository 🤗	https://huggingface.co/S-miguel/The-Trinity-Coder-7B
Merged Model	Yes
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-07-27
Maintainer	S-miguel
Model Type	mistral
Model Files	2.0 GB: 1-of-8 1.9 GB: 2-of-8 2.0 GB: 3-of-8 2.0 GB: 4-of-8 1.9 GB: 5-of-8 1.9 GB: 6-of-8 1.9 GB: 7-of-8 0.8 GB: 8-of-8
Supported Languages	en
Generates Code	Yes
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the The Trinity Coder 7B capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to The Trinity Coder 7B

Best Alternatives	Context / RAM	Downloads	Likes
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
Lexi Coder V5.1	32K / 14.5 GB	197	0
Threebird 7B	32K / 14.5 GB	6	0
Mistral 7B Merged	32K / 14.5 GB	6	0
RP Coder SM3	32K / 14.5 GB	65	0
Jett W26 Abliterated	32K / 14.4 GB	8	1
Jett W26	32K / 14.5 GB	5	1
Ultra Llm Merged	32K / 14.5 GB	7	0
MonarchCoder 7B	32K / 14.4 GB	72	1

Note: green Score (e.g. "73.2") means that the model is better than S-miguel/The-Trinity-Coder-7B.