What are the hardware requirements for PRIME DeCyphers Final?

PRIME DeCyphers Final requires approximately 5.1 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 PRIME DeCyphers Final and how large is it?

PRIME DeCyphers Final is developed by Klevin, 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 PRIME DeCyphers Final?

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

PRIME DeCyphers Final by Klevin — VRAM 5.1GB, 8K context

Name: PRIME DeCyphers Final
Author: Klevin

PRIME DeCyphers Final is an open-source language model by Klevin. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

4bit Base model:finetune:unsloth/ge... Base model:unsloth/gemma-2b-bn... En Endpoints compatible Gemma Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Klevin/PRIME-DeCyphers-Final

PRIME DeCyphers Final Parameters and Internals

Model Type

text-generation-inference, transformers

Additional Notes

This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.

LLM Name	PRIME DeCyphers Final
Repository 🤗	https://huggingface.co/Klevin/PRIME-DeCyphers-Final
Base Model(s)	Gemma 2B Bnb 4bit unsloth/gemma-2b-bnb-4bit
Model Size	2b
Required VRAM	5.1 GB
Updated	2026-07-15
Maintainer	Klevin
Model Type	gemma
Model Files	5.0 GB: 1-of-2 0.1 GB: 2-of-2
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	bfloat16

Rank the PRIME DeCyphers Final 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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... 2B It Hermes Function Calling	8K / 5.1 GB	18	0
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... 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 Klevin/PRIME-DeCyphers-Final.