Mistral 7B Instruct V0.2 NER RE Qlora 1200docs by text2tech

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  Arxiv:1910.09700   Autotrain compatible   Conversational Dataset:text2tech/ner 100abstr... Dataset:text2tech/ner re 1000 ...   Endpoints compatible   Instruct   Mistral   Named entity recognition   Region:us   Relation extraction   Safetensors   Sharded   Tensorflow

Mistral 7B Instruct V0.2 NER RE Qlora 1200docs Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mistral 7B Instruct V0.2 NER RE Qlora 1200docs (text2tech/mistral-7b-instruct-v0.2-NER-RE-qlora-1200docs)

Mistral 7B Instruct V0.2 NER RE Qlora 1200docs Parameters and Internals

Additional Notes 
Mistral fine-tuned on 1000 GPT3.5- and 200 GPT4-labeled documents to extract technical entities and relations between entities from texts.
Training Details 
Data Sources:
text2tech/ner_re_1000_texts_GPT3.5labeled_chat_dataset, text2tech/ner_100abstracts_100full_texts_GPT4labeled_chat_dataset
LLM NameMistral 7B Instruct V0.2 NER RE Qlora 1200docs
Repository ๐Ÿค—https://huggingface.co/text2tech/mistral-7b-instruct-v0.2-NER-RE-qlora-1200docs 
Model Size7b
Required VRAM14.4 GB
Updated2025-02-22
Maintainertext2tech
Model Typemistral
Instruction-BasedYes
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.40.0.dev0
Tokenizer ClassLlamaTokenizer
Vocabulary Size32000
Torch Data Typefloat16

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Rank the Mistral 7B Instruct V0.2 NER RE Qlora 1200docs 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  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227