Alibaba-NLP/gte-Qwen2-7B-instruct
Primitive: /encode · Encode ·
Qwen2
gte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family that ranks No.1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark MTEB benchmark (as of June 16, 2024).
Long contextDense
Overview
Hardware: — drives latency, throughput & cost
| Size | 7.6B params |
|---|---|
| Tasks | /encode |
| License | apache-2.0 |
| Latency | 846 ms |
| Throughput | 3.5K tok/s |
| Cost | $0.063 /1M tok |
Cost is approximate — computed from list GPU prices; your actual price depends on the provider you deploy SIE with.
Embedding
| Output types | Dense |
|---|---|
| Dimensions | dense: 3,584 |
| Max sequence length | 32,000 |
| Inputs | text |
Benchmarks
NFCorpus
Biomedical literature search from NutritionFacts.org
Corpus: 3,593 Queries: 323
Quality
ndcg at 10 0.4040
map at 10 0.1548
mrr at 10 0.6133
Performance L4 b1 c16
Corpus 3.7K tok/s
Corpus p50 1.1s
Query 228 tok/s
Query p50 361.2ms
NanoFiQA2018Retrieval
Smaller subset of the FiQA financial QA dataset
Quality
ndcg at 10 0.6902
map at 10 0.6156
mrr at 10 0.7338
Performance L4 b1 c16
Corpus 3.3K tok/s
Corpus p50 594.5ms
Query 500 tok/s
Query p50 221.7ms
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