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GritLM/GritLM-7B

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Primitive: /encode · Encode · Mistral

> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.

Dense

Overview

Hardware: — drives latency, throughput & cost

Size7.2B params
Tasks /encode
Licenseapache-2.0
Latency2.1 s
Throughput1.4K tok/s
Cost$0.157 /1M tok

Cost is approximate — computed from list GPU prices; your actual price depends on the provider you deploy SIE with.

Embedding

Output typesDense
Dimensionsdense: 4,096
Max sequence length4,096
Inputstext

Benchmarks

NFCorpus

medical retrieval en

Biomedical literature search from NutritionFacts.org

Corpus: 3,593 Queries: 323
Quality
ndcg at 10 0.3972
map at 10 0.1531
mrr at 10 0.6139
Performance L4 b1 c16
Corpus 1.7K tok/s
Corpus p50 2.7s
Query 312 tok/s
Query p50 206.2ms
Reference →

NanoFiQA2018Retrieval

finance retrieval en

Smaller subset of the FiQA financial QA dataset

Quality
ndcg at 10 0.6289
map at 10 0.5506
mrr at 10 0.6275
Performance L4 b1 c16
Corpus 1.1K tok/s
Corpus p50 1.6s
Query 556 tok/s
Query p50 196.8ms
Reference →

Open source inference for agents

Open-source inference for the models behind your agents. Run it yourself, or let us run it for you.

Github 2.3K

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