What are the hardware requirements for CompletionLlama2GGUF?

CompletionLlama2GGUF requires approximately 4.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed CompletionLlama2GGUF and how large is it?

CompletionLlama2GGUF is developed by SajayR, 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 CompletionLlama2GGUF?

CompletionLlama2GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

CompletionLlama2GGUF by SajayR — VRAM 4.1GB

Name: CompletionLlama2GGUF
Author: SajayR

CompletionLlama2GGUF is an open-source language model by SajayR. Features: 7b LLM, VRAM: 4.1GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.12.

4bit Base model:quantized:unsloth/l... Base model:unsloth/llama-2-7b-... En Endpoints compatible Gguf Llama Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/SajayR/completionLlama2GGUF

CompletionLlama2GGUF Parameters and Internals

Model Type

text-generation-inference

Additional Notes

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

LLM Name	CompletionLlama2GGUF
Repository 🤗	https://huggingface.co/SajayR/completionLlama2GGUF
Base Model(s)	Llama 2 7B Bnb 4bit unsloth/llama-2-7b-bnb-4bit
Model Size	7b
Required VRAM	4.1 GB
Updated	2025-02-04
Maintainer	SajayR
Model Type	llama
Model Files	4.1 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Model Architecture	AutoModel
License	apache-2.0

Rank the CompletionLlama2GGUF 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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Note: green Score (e.g. "73.2") means that the model is better than SajayR/completionLlama2GGUF.