What are the hardware requirements for MHENNcodemath?

MHENNcodemath requires approximately 28.9 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 MHENNcodemath and how large is it?

MHENNcodemath is developed by netcat420, a model with 7.2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate MHENNcodemath?

MHENNcodemath is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MHENNcodemath by netcat420 — VRAM 28.9GB, 32K context

Name: MHENNcodemath
Author: netcat420

MHENNcodemath is an open-source language model by netcat420. Features: 7.2b LLM, VRAM: 28.9GB, Context: 32K, License: mit, Quantized, LLM Explorer Score: 0.1.

Dataset:netcat420/compsci Endpoints compatible Gguf Mistral Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/netcat420/MHENNcodemath

MHENNcodemath Parameters and Internals

Additional Notes

Quantized model 'mhenncodemathQ4_K_M.gguf' fine-tuned for 900 steps. Tested to perform well in Rust.

Training Details

Data Sources:

netcat420/compsci

Hardware Used:

V100 on Google Colab

LLM Name	MHENNcodemath
Repository 🤗	https://huggingface.co/netcat420/MHENNcodemath
Model Size	7.2b
Required VRAM	28.9 GB
Updated	2026-07-30
Maintainer	netcat420
Model Type	mistral
Model Files	4.4 GB 5.0 GB: 1-of-6 4.9 GB: 2-of-6 5.0 GB: 3-of-6 5.0 GB: 4-of-6 4.8 GB: 5-of-6 4.2 GB: 6-of-6
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	MistralForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float32

Rank the MHENNcodemath 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 MHENNcodemath

Best Alternatives	Context / RAM	Downloads	Likes
Spydaz Web AGI DeepThink	128K / 14.4 GB	14	1
...daz Web AI Pre Train Align 004	128K / 14.4 GB	28	0
Zion Alpha	32K / 14.4 GB	57	4
LCARS AI 001	32K / 14.4 GB	14	4
Mixtral Quantized	32K / 14.4 GB	6	0
Mayonnaise 4in1 02	32K / 14.4 GB	105	0
Zsql En Postgres	32K / 14.4 GB	64	5
MHENNlitv3	32K / 28.9 GB	12	0
MHENNlitv2	32K / 28.9 GB	17	0
MHENNcodemathv2	32K / 28.9 GB	10	0

Note: green Score (e.g. "73.2") means that the model is better than netcat420/MHENNcodemath.