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Swallow 70B Instruct GGUF by TheBloke

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Base model:quantized:tokyotech... Base model:tokyotech-llm/swall...   En   Gguf   Instruct   Ja   Llama   Quantized   Region:us

Swallow 70B Instruct GGUF Benchmarks

LLME Score: 0.16932
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Swallow 70B Instruct GGUF Parameters and Internals

Model Type 
text-generation
Additional Notes 
The model includes supervised fine-tuning with Japanese data for better performance in Japanese tasks.
Supported Languages 
Japanese (high, additional data), English (standard)
Training Details 
Data Sources:
Japanese Wikipedia, RefinedWeb, Swallow Corpus, The Pile, Anthropic HH-RLHF, Databricks Dolly 15-k, OpenAssistant Conversations Dataset
Methodology:
Continual pre-training and Instruction tuning
Model Architecture:
LLaMA-2 architecture
Input Output 
Input Format:
Japanese Prompt Template
Accepted Modalities:
text
Output Format:
Text
Performance Tips:
Using prompt template in Japanese improves efficiency.
LLM NameSwallow 70B Instruct GGUF
Repository 🤗https://huggingface.co/TheBloke/Swallow-70B-instruct-GGUF 
Model NameSwallow 70B Instruct
Model Creatortokyotech-llm
Base Model(s)  Swallow 70B Instruct Hf   tokyotech-llm/Swallow-70b-instruct-hf
Model Size70b
Required VRAM29.4 GB
Updated2024-11-21
MaintainerTheBloke
Model Typellama
Instruction-BasedYes
Model Files  29.4 GB   36.3 GB   33.3 GB   30.0 GB   39.0 GB   41.5 GB   39.2 GB   47.6 GB   48.9 GB   47.6 GB
Supported Languagesen ja
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureAutoModel
Licensellama2
Swallow 70B Instruct GGUF (TheBloke/Swallow-70B-instruct-GGUF)

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Swallow-70B-instruct-GGUF.

Rank the Swallow 70B Instruct GGUF 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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Release v20241110
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