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Swallow 70B Instruct V0.1 by tokyotech-llm

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  Autotrain compatible   Conversational   En   Endpoints compatible   Instruct   Ja   Llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v0.1 

Swallow 70B Instruct V0.1 Benchmarks

LLME Score: 0.15535
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 70B Instruct V0.1 Parameters and Internals

Model Type 
text-generation, instruction-tuned
Use Cases 
Areas:
Research, Development
Applications:
Cross-Lingual Adaptation, Instruction Following
Primary Use Cases:
Text Generation, Language Translation
Limitations:
Not fine-tuned for specific human intent and safety considerations
Additional Notes 
Developed by multiple team members from TokyoTech-LLM, with acknowledgements to Meta Research for Llama 2.
Supported Languages 
Japanese (Proficient), English (Proficient)
Training Details 
Data Sources:
OpenAssistant Conversations Dataset EN top-1 thread, OpenAssistant Conversations Dataset
Methodology:
Supervised fine-tuning (SFT)
Model Architecture:
Please refer to LLaMA-2 technical report for details on the model architecture.
Input Output 
Input Format:
~~[INST] <> {SYSTEM_PROMPT} <> {USER_MESSAGE} [/INST]
Accepted Modalities:
text
Output Format:
Strings
Performance Tips:
Adhere strictly to instruction format to maintain performance.
Release Notes 
Version:
0.1
Date:
April 26, 2024
Notes:
Release of enhanced instruction-tuned models as preview versions.
Version:
7b-plus
Date:
March 2, 2024
Notes:
Trained with approximately twice as many Japanese tokens.
Version:
13b-NVE-hf
Date:
February 4, 2024
Notes:
Model release with no vocabulary expansion.
Version:
7b-NVE
Date:
January 26, 2024
Notes:
Release of various instruct-hf models as well as no vocabulary expansion models.
Version:
7b
Date:
December 19, 2023
Notes:
Initial release of Swallow 7b, 13b, and 70b in instruct hf variants.
LLM NameSwallow 70B Instruct V0.1
Repository 🤗https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v0.1 
Model Size70b
Required VRAM139.1 GB
Updated2025-06-09
Maintainertokyotech-llm
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB: 1-of-29   4.7 GB: 2-of-29   5.0 GB: 3-of-29   5.0 GB: 4-of-29   4.7 GB: 5-of-29   4.7 GB: 6-of-29   4.7 GB: 7-of-29   5.0 GB: 8-of-29   5.0 GB: 9-of-29   4.7 GB: 10-of-29   4.7 GB: 11-of-29   4.7 GB: 12-of-29   5.0 GB: 13-of-29   5.0 GB: 14-of-29   4.7 GB: 15-of-29   4.7 GB: 16-of-29   4.7 GB: 17-of-29   5.0 GB: 18-of-29   5.0 GB: 19-of-29   4.7 GB: 20-of-29   4.7 GB: 21-of-29   4.7 GB: 22-of-29   5.0 GB: 23-of-29   5.0 GB: 24-of-29   4.7 GB: 25-of-29   4.7 GB: 26-of-29   4.7 GB: 27-of-29   5.0 GB: 28-of-29   4.0 GB: 29-of-29
Supported Languagesen ja
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size43176
Torch Data Typebfloat16
Swallow 70B Instruct V0.1 (tokyotech-llm/Swallow-70b-instruct-v0.1)

Quantized Models of the Swallow 70B Instruct V0.1

Model
Likes
Downloads
VRAM
Swallow 70B Instruct V0.1 4bit12239 GB

Best Alternatives to Swallow 70B Instruct V0.1

Best Alternatives
Context / RAM
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... Chat 1048K Chinese Llama3 70B1024K / 141.9 GB61285
... Chat 1048K Chinese Llama3 70B1024K / 141.9 GB40355
... 3 70B Instruct Gradient 1048K1024K / 141.9 GB38122
Llama3 Function Calling 1048K1024K / 141.9 GB221
...a 3 70B Instruct Gradient 524K512K / 141.9 GB2323
...a 3 70B Instruct Gradient 262K256K / 141.9 GB2355
...ama 3 70B Arimas Story RP V2.0256K / 141.1 GB143
...ama 3 70B Arimas Story RP V1.6256K / 141.2 GB110
...ama 3 70B Arimas Story RP V1.5256K / 141.2 GB142
Llama 3.1 70B Instruct128K / 141.9 GB1297204817
Note: green Score (e.g. "73.2") means that the model is better than tokyotech-llm/Swallow-70b-instruct-v0.1.

Rank the Swallow 70B Instruct V0.1 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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