What are the hardware requirements for Model?

Model requires approximately 17.1 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 Model and how large is it?

Model is developed by taradepan, 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 Model?

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

Model by taradepan — VRAM 17.1GB, 8K context

Name: Model
Author: taradepan

Model is an open-source language model by taradepan. Features: 7b LLM, VRAM: 17.1GB, Context: 8K, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

4bit Base model:finetune:unsloth/ge... Base model:unsloth/gemma-7b-bn... En Endpoints compatible Gemma Pytorch Quantized Region:us Sft Sharded Trl Unsloth

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

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	Model
Repository 🤗	https://huggingface.co/taradepan/model
Base Model(s)	Gemma 7B Bnb 4bit unsloth/gemma-7b-bnb-4bit
Model Size	7b
Required VRAM	17.1 GB
Updated	2026-08-08
Maintainer	taradepan
Model Type	gemma
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.1 GB: 4-of-4
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 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 Model

Best Alternatives	Context / RAM	Downloads	Likes
...t Cleaner Gemma 32k Merged 16b	31K / 17.1 GB	7	0
...ruct Malayalam Model Vllm 4bit	16K / 5.6 GB	5	0
Gemma 1.1 7B It 4bit	8K / 5.9 GB	68	4
Gemma 1.1 7B It 4bit	8K / 5.9 GB	5	1
Gemma 7B 8bit	8K / 9.4 GB	3	1
... Codegemma 2 7B It Alpaca V1.2	8K / 17.1 GB	35	0
Codegemma 7B It 8bit	8K / 9.5 GB	203	6
Fbt Gemma 7B	8K / 17.1 GB	18	0
Codegemma 7B It 4bit	8K / 5.9 GB	89	2
Codegemma 7B 4bit	8K / 5.9 GB	48	0

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