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: 0.08.
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.
Note: green Score (e.g. "73.2") means that the model is better than 4bit/falcon-7b-instruct-GPTQ.
Rank the Falcon 7B Instruct GPTQ Capabilities
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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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