F2LLM-0.6B

278
11
600M
1 language
license:apache-2.0
by
codefuse-ai
Embedding Model
OTHER
0.6B params
New
278 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
2GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

grad_fn=<MmBackward0>)python
import mteb
import logging
logging.basicConfig(level=logging.INFO)

task_names = ['AmazonCounterfactualClassification', 'ArXivHierarchicalClusteringP2P', 'ArXivHierarchicalClusteringS2S', 'ArguAna', 'AskUbuntuDupQuestions', 'BIOSSES', 'Banking77Classification', 'BiorxivClusteringP2P.v2', 'CQADupstackGamingRetrieval', 'CQADupstackUnixRetrieval', 'ClimateFEVERHardNegatives', 'FEVERHardNegatives', 'FiQA2018', 'HotpotQAHardNegatives', 'ImdbClassification', 'MTOPDomainClassification', 'MassiveIntentClassification', 'MassiveScenarioClassification', 'MedrxivClusteringP2P.v2', 'MedrxivClusteringS2S.v2', 'SCIDOCS', 'SICK-R', 'STS12', 'STS13', 'STS14', 'STS15', 'STS17', 'STS22.v2', 'STSBenchmark', 'SprintDuplicateQuestions', 'StackExchangeClustering.v2', 'StackExchangeClusteringP2P.v2', 'SummEvalSummarization.v2', 'TRECCOVID', 'Touche2020Retrieval.v3', 'ToxicConversationsClassification', 'TweetSentimentExtractionClassification', 'TwentyNewsgroupsClustering.v2', 'TwitterSemEval2015', 'TwitterURLCorpus', 'MindSmallReranking']

tasks = [
    mteb.get_task(task_name, languages = ["eng"], eval_splits=["test"], exclusive_language_filter=True)
    for task_name in task_names
]


model = mteb.get_model("codefuse-ai/F2LLM-0.6B", device="cuda:0")
evaluation = mteb.MTEB(tasks=tasks)
evaluation.run(model, encode_kwargs={"batch_size": 16})

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