What are the hardware requirements for Elm Test?

Elm Test requires approximately 13.6 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Elm Test and how large is it?

Elm Test is developed by TinyPixel, 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 Elm Test perform on standard benchmarks?

Elm Test has the following published scores: MMLU 47.04. Compare against reference models on this page or on the LLM Explorer leaderboards.

Elm Test by TinyPixel — VRAM 13.6GB, 2K context

Name: Elm Test
Author: TinyPixel

Elm Test is an open-source language model by TinyPixel. Features: 7b LLM, VRAM: 13.6GB, Context: 2K, LLM Explorer Score: 0.13, Arc: 53.2, HellaSwag: 79, MMLU: 47, GSM8K: 7.5.

Endpoints compatible Llama Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/TinyPixel/elm-test

Elm Test Benchmarks

ARC: 53.16 vs 96.7 (so35)^-45%

HellaSwag: 78.98 vs 95.3 (gpt4)^-17.1%

MMLU: 47.04 vs 88.3 (so35)^-46.7%

GSM8K: 7.51 vs 96.4 (so35)^-92.2%

LLME Score: 0.12952

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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Elm Test Parameters and Internals

LLM Name	Elm Test
Repository 🤗	https://huggingface.co/TinyPixel/elm-test
Model Size	7b
Required VRAM	13.6 GB
Updated	2026-05-14
Maintainer	TinyPixel
Model Type	llama
Model Files	1.0 GB: 1-of-14 1.0 GB: 2-of-14 1.0 GB: 3-of-14 1.0 GB: 4-of-14 0.9 GB: 5-of-14 1.0 GB: 6-of-14 1.0 GB: 7-of-14 1.0 GB: 8-of-14 1.0 GB: 9-of-14 0.9 GB: 10-of-14 1.0 GB: 11-of-14 1.0 GB: 12-of-14 1.0 GB: 13-of-14 0.8 GB: 14-of-14 1.0 GB: 1-of-14 1.0 GB: 2-of-14 1.0 GB: 3-of-14 1.0 GB: 4-of-14 0.9 GB: 5-of-14 1.0 GB: 6-of-14 1.0 GB: 7-of-14 1.0 GB: 8-of-14 1.0 GB: 9-of-14 0.9 GB: 10-of-14 1.0 GB: 11-of-14 1.0 GB: 12-of-14 1.0 GB: 13-of-14 0.8 GB: 14-of-14
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.34.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Best Alternatives to Elm Test

Best Alternatives	Context / RAM	Downloads
124	1024K / 16.1 GB	93
162	1024K / 16.1 GB	60
157	1024K / 16.1 GB	101
118	1024K / 16.1 GB	15
A5.4	1024K / 16.1 GB	12
A3.4	1024K / 16.1 GB	13
A2.4	1024K / 16.1 GB	12
A6 L	1024K / 16.1 GB	201
M	1024K / 16.1 GB	127
2 Very Sci Fi	1024K / 16.1 GB	317

Note: green Score (e.g. "73.2") means that the model is better than TinyPixel/elm-test.

Rank the Elm Test Capabilities

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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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Elm Test by TinyPixel

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Elm Test Benchmarks

Elm Test Parameters and Internals

Best Alternatives to Elm Test

Rank the Elm Test Capabilities

What open-source LLMs or SLMs are you in search of? 54323 in total.