What are the hardware requirements for Acemath 200?

Acemath 200 requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Acemath 200 and how large is it?

Acemath 200 is developed by NikolaSigmoid, a model with 1.5b 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 Acemath 200?

Acemath 200 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Acemath 200 by NikolaSigmoid — VRAM 0.1GB

Name: Acemath 200
Author: NikolaSigmoid

Acemath 200 is an open-source language model by NikolaSigmoid. Features: 1.5b LLM, VRAM: 0.1GB, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.22.

Base model:finetune:nvidia/ace... Base model:nvidia/acemath-1.5b... Conversational En Endpoints compatible Grpo Instruct Lora Pytorch Qwen2 Region:us Safetensors Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/NikolaSigmoid/acemath-200

Acemath 200 Parameters and Internals

LLM Name	Acemath 200
Repository 🤗	https://huggingface.co/NikolaSigmoid/acemath-200
Base Model(s)	nvidia/AceMath-1.5B-Instruct nvidia/AceMath-1.5B-Instruct
Model Size	1.5b
Required VRAM	0.1 GB
Updated	2026-01-01
Maintainer	NikolaSigmoid
Instruction-Based	Yes
Model Files	0.1 GB 3.1 GB
Supported Languages	en
Model Architecture	AutoModelForCausalLM
License	apache-2.0
Model Max Length	4096
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_proj\|up_proj\|down_proj
LoRA Alpha	16
LoRA Dropout	0
R Param	16
Errors	replace

Rank the Acemath 200 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 Acemath 200

Best Alternatives	Context / RAM	Downloads
AceMath 1.5B Instruct 1epoch	0K / 0.1 GB	7
Qwen First Aid Medium	0K / 1.9 GB	0
MeoinGTS1.5 1.5B	0K / 1 GB	147

Note: green Score (e.g. "73.2") means that the model is better than NikolaSigmoid/acemath-200.