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Slim Nli by llmware

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

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  Autotrain compatible   Llama   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/llmware/slim-nli 

Slim Nli Benchmarks

LLME Score: 0.16691
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Slim Nli (llmware/slim-nli)

Slim Nli Parameters and Internals

Model Type 
decoder-based, function-calling, natural language inference (NLI)
Use Cases 
Applications:
function-calling, multi-step automation workflow
Primary Use Cases:
natural language inference (NLI)
Additional Notes 
SLIM models are quantized and specialized for structured output generation.
Input Output 
Input Format:
function = "classify" params = "nli" prompt = " " + {text} + "\n" + "<{function}> " + {params} + "" + "\n:"
Accepted Modalities:
text
Output Format:
Python dictionary
LLM NameSlim Nli
Repository 🤗https://huggingface.co/llmware/slim-nli 
Model Size1.1b
Required VRAM2.2 GB
Updated2024-12-22
Maintainerllmware
Model Typellama
Model Files  2.2 GB
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.36.1
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than llmware/slim-nli.

Rank the Slim Nli 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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