Swallow 7B AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Base model:quantized:tokyotech... Base model:tokyotech-llm/swall...   En   Ja   Llama   Quantized   Region:us   Safetensors
Model Card on HF ๐Ÿค—: https://huggingface.co/TheBloke/Swallow-7B-AWQ 

Swallow 7B AWQ Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Swallow 7B AWQ Parameters and Internals

Model Type 
text-generation
Supported Languages 
en (English), ja (Japanese)
Training Details 
Data Sources:
Japanese Wikipedia, RefinedWeb, Swallow Corpus, The Pile
Context Length:
4096
Hardware Used:
Massed Compute's hardware
Model Architecture:
LLaMA-2
Input Output 
Input Format:
{prompt}
Accepted Modalities:
text
LLM NameSwallow 7B AWQ
Repository ๐Ÿค—https://huggingface.co/TheBloke/Swallow-7B-AWQ 
Model NameSwallow 7B
Model Creatortokyotech-llm
Base Model(s)  Swallow 7B Hf   tokyotech-llm/Swallow-7b-hf
Model Size7b
Required VRAM4.1 GB
Updated2025-06-09
MaintainerTheBloke
Model Typellama
Model Files  4.1 GB
Supported Languagesen ja
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size43176
Torch Data Typefloat16
Swallow 7B AWQ (TheBloke/Swallow-7B-AWQ)

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Swallow-7B-AWQ.

Rank the Swallow 7B AWQ 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 v20241124