What are the hardware requirements for Test Model?

Test Model requires approximately 2.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Test Model and how large is it?

Test Model is developed by taradepan, a model with 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 Test Model?

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

Test Model by taradepan — VRAM 2.1GB, 8K context

Name: Test Model
Author: taradepan

Test Model is an open-source language model by taradepan. Features: 2b LLM, VRAM: 2.1GB, Context: 8K, License: apache-2.0, Quantized, LLM Explorer Score: 0.12.

4-bit 4bit Base model:quantized:unsloth/g... Base model:unsloth/gemma-2b-bn... Bitsandbytes En Endpoints compatible Gemma Quantized Region:us Safetensors Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/taradepan/test-model

Test Model Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, gemma, trl, sft

Additional Notes

The model was finetuned from unsloth/gemma-7b-bnb-4bit

Training Details

Methodology:

Unsuloth and Huggingface's TRL library were used to train the model 2x faster.

LLM Name	Test Model
Repository 🤗	https://huggingface.co/taradepan/test-model
Base Model(s)	Gemma 2B Bnb 4bit unsloth/gemma-2b-bnb-4bit
Model Size	2b
Required VRAM	2.1 GB
Updated	2026-08-06
Maintainer	taradepan
Model Type	gemma
Model Files	2.1 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	GemmaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.41.2
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Test Model 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 Test Model

Best Alternatives	Context / RAM	Downloads	Likes
... 2B It Hermes Function Calling	8K / 5.1 GB	18	0
Vi Gemma 2B RAG	8K / 5.1 GB	36	13
My AwesomeFinance Model	8K / 2.1 GB	12	0
... 2.8 Gemma 2B HQQ 1bit Smashed	8K / 1.3 GB	5	0
... 2.8 Gemma 2B HQQ 2bit Smashed	8K / 1.6 GB	5	0
... 2.8 Gemma 2B HQQ 4bit Smashed	8K / 2.1 GB	5	0
Emo AI 3B	8K / 5.1 GB	6	1
Jenna V3 QGemma 2B	8K / 5.1 GB	6	0
Gemma 2B Sentiment	8K / 5.1 GB	13	0
Jenna V2	8K / 5.1 GB	6	0

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