What are the hardware requirements for Lima Test?

Lima 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 Lima Test and how large is it?

Lima 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 Lima Test perform on standard benchmarks?

Lima Test has the following published scores: MMLU 46.42. Compare against reference models on this page or on the LLM Explorer leaderboards.

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

Name: Lima Test
Author: TinyPixel

Lima Test is an open-source language model by TinyPixel. Features: 7b LLM, VRAM: 13.6GB, Context: 2K, LLM Explorer Score: 0.11, Arc: 53.1, HellaSwag: 78.9, MMLU: 46.4, GSM8K: 8.

Endpoints compatible Llama Pytorch Region:us Safetensors Sharded Tensorflow

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

Lima Test Parameters and Internals

LLM Name	Lima Test
Repository 🤗	https://huggingface.co/TinyPixel/lima-test
Model Size	7b
Required VRAM	13.6 GB
Updated	2026-07-23
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	4.33.0.dev0
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Lima Test 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 Lima 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/lima-test.