What are the hardware requirements for Acemath Convfinqa?

Acemath Convfinqa requires approximately 15.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Acemath Convfinqa and how large is it?

Acemath Convfinqa is developed by tslim1, a model with 7b 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 Convfinqa?

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

Acemath Convfinqa by tslim1 — VRAM 15.2GB

Name: Acemath Convfinqa
Author: tslim1

Acemath Convfinqa is an open-source language model by tslim1. Features: 7b LLM, VRAM: 15.2GB, Instruction-Based, LLM Explorer Score: 0.19.

Base model:finetune:nvidia/ace... Base model:nvidia/acemath-7b-i... Conversational Endpoints compatible Generated from trainer Instruct Lora Qwen2 Region:us Safetensors Sft Sharded Tensorflow Trl

Model Card on HF 🤗: https://huggingface.co/tslim1/acemath-convfinqa

Acemath Convfinqa Parameters and Internals

LLM Name	Acemath Convfinqa
Repository 🤗	https://huggingface.co/tslim1/acemath-convfinqa
Model Name	acemath-convfinqa
Base Model(s)	AceMath 7B Instruct nvidia/AceMath-7B-Instruct
Model Size	7b
Required VRAM	15.2 GB
Updated	2026-07-18
Maintainer	tslim1
Instruction-Based	Yes
Model Files	0.2 GB 4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4 0.0 GB
Model Architecture	AutoModelForCausalLM
Model Max Length	131072
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_proj\|v_proj\|up_proj\|o_proj\|q_proj\|k_proj\|down_proj
LoRA Alpha	32
LoRA Dropout	0.1
R Param	16
Errors	replace

Rank the Acemath Convfinqa 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

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Mistral 7B Instruct V0.3	32K / 14.5 GB	91	6
Mistral 7B Instruct V0.3	32K / 14.5 GB	1122	3
...ralai Mistral 7B Instruct V0.3	32K / 14.5 GB	6	3
Mistral 7B Instruct V0.3	32K / 14.5 GB	6	0
Full V4 Astromistral Final	32K / 4.5 GB	6	1
Zyr3 V1	0K / 0.1 GB	206	1
Dekillnovelist V2	0K / 0.2 GB	203	0
Mistral 7B Manumit V1	0K / 14.5 GB	130	0
Mistral 7B Instruct Uz	0K / 14.5 GB	59	15
Qwen Medical 7B	0K / 15.4 GB	24	0

Note: green Score (e.g. "73.2") means that the model is better than tslim1/acemath-convfinqa.