gte-Qwen2-7B-instruct-GGUF Open-Source Large Language Model - A Practical AI Tool Supporting Instruction Interaction

Gte Qwen2 7B Instruct GGUF

Developed by tensorblock

A large language model developed by Alibaba NLP team, based on the Qwen2 architecture with 7B parameters, supporting instruction interaction

Large Language Model Open Source License:Apache-2.0 #Lightweight GGUF #Chinese instruction fine-tuning #Multi-turn dialogue optimization

Downloads 1,502

Release Time : 11/11/2024

Model Overview

This is a 7B-parameter instruction-tuned large language model suitable for natural language understanding and generation tasks, compatible with the llama.cpp inference framework

Model Features

Multiple quantization versions

Provides 12 quantization versions from Q2_K to Q8_0 to meet inference needs under different hardware conditions

Instruction optimization

Specifically optimized for instruction interaction scenarios, better understanding and executing user instructions

llama.cpp compatibility

The quantized model is fully compatible with the llama.cpp inference framework for easy deployment

Model Capabilities

Text generation

Instruction understanding

Dialogue interaction

Text summarization

Q&A systems

Use Cases

Intelligent assistant

Virtual customer service

Used for e-commerce platform automated customer service systems

Can understand user questions and provide accurate answers

Content generation

Article creation

Assisting content creators in generating drafts

Can generate coherent and logical text content

🚀 gte-qwen2-7B-instruct

This is a model based on Alibaba-NLP/gte-Qwen2-7B-instruct, which has shown excellent performance in multiple tasks of the MTEB benchmark, such as classification, retrieval, clustering, reranking, and STS.

✨ Features

Rich Tags: Related to mteb, sentence-transformers, transformers, Qwen2, sentence-similarity, TensorBlock, GGUF.
Multiple Task Performance: Demonstrates performance in various tasks including classification, retrieval, clustering, reranking, and STS.

📚 Documentation

Model Information

Property	Details
Model Type	gte-qwen2-7B-instruct
Base Model	Alibaba-NLP/gte-Qwen2-7B-instruct
License	apache-2.0

Performance Metrics

1. Classification Tasks

Dataset	Accuracy	AP	F1
MTEB AmazonCounterfactualClassification (en)	91.31343283582089	67.64251402604096	87.53372530755692
MTEB AmazonPolarityClassification	97.497825	96.30329547047529	97.49769793778039
MTEB AmazonReviewsClassification (en)	62.564	-	60.975777935041066
MTEB Banking77Classification	87.56818181818181	-	87.25826722019875

2. Retrieval Tasks

Dataset	MAP@1	MAP@10	MAP@100	MAP@1000	MRR@1	MRR@10	MRR@100	MRR@1000	NDCG@1	NDCG@10	NDCG@100	NDCG@1000	Precision@1	Precision@10	Precision@100	Precision@1000	Recall@1	Recall@10	Recall@100	Recall@1000
MTEB ArguAna	36.486000000000004	54.842	55.206999999999994	55.206999999999994	37.34	55.143	55.509	55.509	36.486000000000004	64.273	65.66199999999999	65.66199999999999	36.486000000000004	9.395000000000001	0.996	0.1	36.486000000000004	93.95400000000001	99.644	99.644
MTEB CQADupstackAndroidRetrieval	33.997	48.176	49.82	49.924	42.059999999999995	53.726	54.398	54.416	42.059999999999995	55.574999999999996	60.744	61.85699999999999	42.059999999999995	11.101999999999999	1.73	0.218	33.997	70.35900000000001	91.642	97.977
MTEB CQADupstackEnglishRetrieval	35.884	48.14	49.5	49.63	44.458999999999996	53.751000000000005	54.37800000000001	54.415	44.458999999999996	54.157	58.362	60.178	44.458999999999996	10.248	1.5890000000000002	0.207	35.884	64.798	82.345	93.267
MTEB CQADupstackGamingRetrieval	39.383	53.714	54.838	54.87800000000001	45.016	56.732000000000006	57.411	57.431	45.016	60.228	64.277	65.07	45.016	9.937	1.288	0.13899999999999998	39.383	76.175	93.02	98.60900000000001
MTEB CQADupstackGisRetrieval	27.426000000000002	37.397000000000006	38.61	38.678000000000004	29.944	39.654	40.638000000000005	40.691	29.944	43.094	48.789	50.339999999999996	29.944	6.78	1.024	0.11800000000000001	27.426000000000002	58.464000000000006	84.193	95.52000000000001
MTEB CQADupstackMathematicaRetrieval	19.721	31.604	32.972	33.077	25.0	35.843	36.785000000000004	36.842000000000006	25.0	38.606	44.272	46.527	25.0	6.321	0.938	0.102	19.721	50.246	78.567	92.345

3. Clustering Tasks

Dataset	V-Measure
MTEB ArxivClusteringP2P	56.461169803700564
MTEB ArxivClusteringS2S	51.73600434466286
MTEB BiorxivClusteringP2P	50.09239610327673
MTEB BiorxivClusteringS2S	46.64733054606282

4. Reranking Tasks

Dataset	MAP	MRR
MTEB AskUbuntuDupQuestions	67.57827065898053	79.08136569493911

5. STS Tasks

Dataset	Cosine Similarity Pearson	Cosine Similarity Spearman	Euclidean Pearson	Euclidean Spearman	Manhattan Pearson	Manhattan Spearman
MTEB BIOSSES	83.53324575999243	81.37173362822374	82.19243335103444	81.33679307304334	82.38752665975699	81.31510583189689

📄 License

This project is licensed under the apache-2.0 license.

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