What are the hardware requirements for Falcon 7B Instruct GPTQ?

Falcon 7B Instruct GPTQ requires approximately 5.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Falcon 7B Instruct GPTQ and how large is it?

Falcon 7B Instruct GPTQ is developed by 4bit, 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 Falcon 7B Instruct GPTQ?

Falcon 7B Instruct GPTQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Falcon 7B Instruct GPTQ by 4bit — VRAM 5.9GB

Name: Falcon 7B Instruct GPTQ
Author: 4bit

Falcon 7B Instruct GPTQ is an open-source language model by 4bit. Features: 7b LLM, VRAM: 5.9GB, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.07.

Arxiv:1911.02150 Arxiv:2005.14165 Arxiv:2104.09864 Arxiv:2205.14135 4bit Custom code Dataset:tiiuae/falcon-refinedw... En Gptq Instruct Quantized Refinedwebmodel Region:us

Model Card on HF 🤗: https://huggingface.co/4bit/falcon-7b-instruct-GPTQ

Falcon 7B Instruct GPTQ Parameters and Internals

Model Type

Causal decoder-only

Additional Notes

Model optimized for inference with FlashAttention and multiquery, based on Falcon-7B with improvements. Experimental model with limited support and expected slow performance.

Training Details

Data Sources:

Bai ze, GPT4All, GPTeacher, RefinedWeb-English

Data Volume:

250M tokens

Methodology:

Finetuned on instruct/chat datasets

Context Length:

2048

Hardware Used:

32 A100 40GB GPUs on AWS SageMaker

Model Architecture:

Causal decoder-only with rotary positionnal embeddings, multiquery and FlashAttention, parallel attention/MLP with a single layer norm.

Input Output

Performance Tips:

Performance expected to be slow due to experimental nature. Improved results with updated AutoGPTQ and specified commit.

Release Notes

Version:

Experimental GPTQ

Notes:

Quantisation to 4bit using AutoGPTQ. Uses groupsize 64 without act-order.

LLM Name	Falcon 7B Instruct GPTQ
Repository 🤗	https://huggingface.co/4bit/falcon-7b-instruct-GPTQ
Base Model(s)	...on 7B Instruct Finance Advisor themanas021/falcon-7b-instruct-Finance_Advisor
Model Size	7b
Required VRAM	5.9 GB
Updated	2026-10-29
Maintainer	4bit
Model Type	RefinedWebModel
Instruction-Based	Yes
Model Files	5.9 GB
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq\|4bit
Model Architecture	RWForCausalLM
License	apache-2.0
Model Max Length	2048
Transformers Version	4.29.2
Is Biased	0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	65024
Torch Data Type	float32

Rank the Falcon 7B Instruct GPTQ 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 Falcon 7B Instruct GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
Falcon 7B Instruct GPTQ	0K / 5.9 GB	20	68
Falcon 7B Instruct Autogptq	0K / 4.8 GB	8	1
Falcon 7B Instruct 8bit	0K / 7.2 GB	5	0
Ct2 Int8 Falcon 7B Instruct	0K / GB	5	0
Falcon 7b Python Instructions	0K / 13.8 GB	5	1
Docsgpt 7B Falcon	0K / 13.8 GB	85	15
Falcon 7B Instruct V2	0K / 28.9 GB	5	1
... Falcon 7B Instruct W4 G64 AWQ	0K / 4.2 GB	18	5
Falcon 7B Finance	0K / 13.8 GB	18	3
Vigogne Falcon 7B Instruct	0K / 14.4 GB	10	1

Note: green Score (e.g. "73.2") means that the model is better than 4bit/falcon-7b-instruct-GPTQ.