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Slim Extract by llmware

 »  All LLMs  »  llmware  »  Slim Extract   URL Share it on

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  Autotrain compatible   Custom code   Pytorch   Region:us   Stablelm epoch
Model Card on HF 🤗: https://huggingface.co/llmware/slim-extract 

Slim Extract Benchmarks

LLME Score: 0.18559
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Slim Extract (llmware/slim-extract)

Slim Extract Parameters and Internals

Model Type 
function-calling, text extraction
Use Cases 
Areas:
Research, development, text analysis
Applications:
Automated extraction, data collation, text processing in Python.
Primary Use Cases:
Extracting specified information keys from text and outputting as Python dictionary.
Additional Notes 
Specializes in structured extractions from text, targeting list outputs for given keys.
Training Details 
Methodology:
Fine-tuning
Model Architecture:
Can perform specialized extractions from text and output Python dictionary.
Input Output 
Input Format:
Context passage and customized key for extraction
Accepted Modalities:
text
Output Format:
Python dictionary
LLM NameSlim Extract
Repository 🤗https://huggingface.co/llmware/slim-extract 
Model Size3b
Required VRAM5.6 GB
Updated2024-12-23
Maintainerllmware
Model Typestablelm_epoch
Model Files  5.6 GB
Model ArchitectureStableLMEpochForCausalLM
Licensecc-by-sa-4.0
Context Length4096
Model Max Length4096
Transformers Version4.33.2
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50304
Torch Data Typebfloat16

Best Alternatives to Slim Extract

Best Alternatives
Context / RAM
Downloads
Likes
Stable Code 3B Mlx16K / 5.6 GB91
Aura 3B4K / 5.6 GB112
Slim Boolean4K / 5.6 GB194
Slim Summary4K / 5.6 GB597
Slim Tags 3B4K / 5.6 GB124
Slim Sa Ner4K / 5.6 GB86
Slim Xsum4K / 5.6 GB56
Memphis CoT 3B4K / 5.6 GB7329
Tofu 3B4K / 5.6 GB102
Fett Uccine Mini 3B4K / 5.6 GB123
Note: green Score (e.g. "73.2") means that the model is better than llmware/slim-extract.

Rank the Slim Extract Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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