What are the hardware requirements for EDGE 0 7B GGML?

EDGE 0 7B GGML requires approximately 2.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed EDGE 0 7B GGML and how large is it?

EDGE 0 7B GGML is developed by EDGE-AI, 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 EDGE 0 7B GGML?

EDGE 0 7B GGML is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

EDGE 0 7B GGML by EDGE-AI — VRAM 2.9GB

Name: EDGE 0 7B GGML
Author: EDGE-AI

EDGE 0 7B GGML is an open-source language model by EDGE-AI. Features: 7b LLM, VRAM: 2.9GB, License: other, Quantized, LLM Explorer Score: 0.08.

En Facebook Ggml Llama Llama2 Meta Pytorch Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/EDGE-AI/EDGE_0-7B_GGML

EDGE 0 7B GGML Parameters and Internals

Model Type

text generation

Use Cases

Areas:

Commercial applications, Research

Primary Use Cases:

Assistant-like chat, Natural language generation tasks

Limitations:

Use in languages other than English

Considerations:

Specific formatting needs to be followed for chat versions. Use in any prohibited manner mentioned in the license.

Additional Notes

This model was part of an open release by Meta with emphasis on commercial and research usability, governed under a specific licensing agreement.

Supported Languages

en (English)

Training Details

Data Sources:

A new mix of publicly available online data

Data Volume:

2 trillion tokens

Methodology:

Auto-regressive model using optimized transformer architecture with supervised fine-tuning and RLHF.

Context Length:

4000

Hardware Used:

Meta's Research Super Cluster, A100-80GB GPUs

Model Architecture:

Uses Grouped-Query Attention (GQA).

Safety Evaluation

Ethical Considerations:

Llama 2 is a new technology that carries risks with use. Inaccurate, biased, or objectionable responses may occur.

Responsible Ai Considerations

Transparency:

Responsible Use Guide provided by Meta.

Input Output

Input Format:

Text input

Accepted Modalities:

text

Output Format:

Generates text

Performance Tips:

Specific formatting needs to be followed for chat versions using appropriate tokens and tags.

LLM Name	EDGE 0 7B GGML
Repository 🤗	https://huggingface.co/EDGE-AI/EDGE_0-7B_GGML
Model Size	7b
Required VRAM	2.9 GB
Updated	2026-07-10
Maintainer	EDGE-AI
Model Type	llama
Model Files	2.9 GB 3.6 GB 3.3 GB 3.0 GB 3.8 GB 4.2 GB 4.1 GB 3.8 GB 4.6 GB 5.1 GB 4.8 GB 4.7 GB 5.5 GB 7.2 GB
Supported Languages	en
GGML Quantization	Yes
Quantization Type	ggml
Model Architecture	AutoModel
License	other

Rank the EDGE 0 7B GGML 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 EDGE 0 7B GGML

Best Alternatives	Context / RAM	Downloads	Likes
Llama 2 7B Chat GGML	0K / 2.9 GB	157	872
Llama 2 GGML Medical Chatbot	0K / GB	14	45
Llama 2 7B GGML	0K / 2.9 GB	147	219
Yarn Llama 2 7B 128K GGML	0K / 2.9 GB	3	6
Yarn Llama 2 7B 64K GGML	0K / 2.9 GB	5	3
CodeLlama 7B GGML	0K / 3 GB	10	27
CodeLlama 7B Python GGML	0K / 2.9 GB	6	24
CodeLlama 7B Instruct GGML	0K / 3 GB	10	20
Airoboros L2 7B 2.1 GGML	0K / 2.9 GB	5	1
Zarafusionex 1.1 L2 7B GGML	0K / 2.9 GB	6	2

Note: green Score (e.g. "73.2") means that the model is better than EDGE-AI/EDGE_0-7B_GGML.