SmolLM2 360M by HuggingFaceTB

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SmolLM2 360M Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
SmolLM2 360M (HuggingFaceTB/SmolLM2-360M)

SmolLM2 360M Parameters and Internals

Model Type 
language model, instruction model
Use Cases 
Primary Use Cases:
text rewriting, summarization, function calling
Limitations:
Primarily understands and generates content in English, Generated content may not be factually accurate or logically consistent, Bias may be present in the training data
Considerations:
Use as assistive tools, always verify critical information
Supported Languages 
en (High proficiency)
Training Details 
Data Sources:
FineWeb-Edu, DCLM, The Stack
Data Volume:
4 trillion tokens
Methodology:
Supervised fine-tuning and Direct Preference Optimization (DPO)
Hardware Used:
64 H100 GPUs
Model Architecture:
Transformer decoder
Input Output 
Accepted Modalities:
text
LLM NameSmolLM2 360M
Repository ๐Ÿค—https://huggingface.co/HuggingFaceTB/SmolLM2-360M 
Model Size360m
Required VRAM0.7 GB
Updated2025-02-05
MaintainerHuggingFaceTB
Model Typellama
Model Files  0.7 GB
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.40.1
Tokenizer ClassGPT2Tokenizer
Vocabulary Size49152
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than HuggingFaceTB/SmolLM2-360M.

Rank the SmolLM2 360M 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  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227