What are the hardware requirements for MonarchCoder 7B?

MonarchCoder 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 MonarchCoder 7B and how large is it?

MonarchCoder 7B is developed by abideen, 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 MonarchCoder 7B perform on standard benchmarks?

MonarchCoder 7B has the following published scores: MMLU 64.65. Compare against reference models on this page or on the LLM Explorer leaderboards.

MonarchCoder 7B by abideen — VRAM 14.4GB, 32K context

Name: MonarchCoder 7B
Author: abideen

MonarchCoder 7B is an open-source language model by abideen. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Code Generating, Merged, LLM Explorer Score: 0.12, Arc: 68.5, HellaSwag: 87.3, MMLU: 64.7, GSM8K: 65.1.

Merged Model Base model:mlabonne/alphamonar... Base model:syed-hasan-8503/tes... Codegen Conversational En Endpoints compatible Mistral Mlabonne/alphamonarch-7b Model-index Region:us Safetensors Sharded Syed-hasan-8503/tess-coder-7b-... Tensorflow

Model Card on HF 🤗: https://huggingface.co/abideen/MonarchCoder-7B

MonarchCoder 7B Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

research, commercial applications

Applications:

reasoning, conversation, coding

Additional Notes

MonarchCoder-7B is optimized for reasoning, conversation, and coding tasks based on alpha model's strengths.

Supported Languages

en (native)

Training Details

Methodology:

Slerp merge using LazyMergekit

Input Output

Input Format:

A prompt formatted using a chat template.

Accepted Modalities:

text

Output Format:

Generated text.

Performance Tips:

Use optimal settings like temperature:0.7, top_k:50, top_p:0.95 for better performance.

LLM Name	MonarchCoder 7B
Repository 🤗	https://huggingface.co/abideen/MonarchCoder-7B
Base Model(s)	Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 AlphaMonarch 7B Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 mlabonne/AlphaMonarch-7B
Merged Model	Yes
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-07-20
Maintainer	abideen
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	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the MonarchCoder 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 MonarchCoder 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
The Trinity Coder 7B	32K / 14.4 GB	63	6

Note: green Score (e.g. "73.2") means that the model is better than abideen/MonarchCoder-7B.