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Swallow 70B Instruct AWQ by TheBloke

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  4-bit   Autotrain compatible   Awq Base model:quantized:tokyotech... Base model:tokyotech-llm/swall...   En   Instruct   Ja   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow

Swallow 70B Instruct AWQ Benchmarks

LLME Score: 0.16584
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 AWQ Parameters and Internals

Model Type 
llama
Use Cases 
Applications:
Research, Commercial applications
Additional Notes 
The Swallow model is mainly tuned for Japanese language data and features modified vocabulary to enhance inference speed.
Supported Languages 
languages_supported (>2 including Japanese and English.), proficiency_notes (>Proficient in Japanese and English.)
Training Details 
Data Sources:
Japanese Wikipedia, RefinedWeb, Swallow Corpus, The Pile
Methodology:
Supervised fine-tuning (SFT)
Context Length:
4096
Model Architecture:
Llama
Input Output 
Input Format:
Formatted Japanese instructions and prompts.
Accepted Modalities:
Text
Output Format:
Generated textual response
LLM NameSwallow 70B Instruct AWQ
Repository 🤗https://huggingface.co/TheBloke/Swallow-70B-instruct-AWQ 
Model NameSwallow 70B Instruct
Model Creatortokyotech-llm
Base Model(s)  Swallow 70B Instruct Hf   tokyotech-llm/Swallow-70b-instruct-hf
Model Size70b
Required VRAM37 GB
Updated2024-11-22
MaintainerTheBloke
Model Typellama
Instruction-BasedYes
Model Files  9.9 GB: 1-of-4   10.0 GB: 2-of-4   9.9 GB: 3-of-4   7.2 GB: 4-of-4
Supported Languagesen ja
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.37.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size43176
Torch Data Typefloat16
Swallow 70B Instruct AWQ (TheBloke/Swallow-70B-instruct-AWQ)

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

Rank the Swallow 70B Instruct 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 v20241110
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