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Swallow 13B Instruct Hf by tokyotech-llm

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Swallow 13B Instruct Hf is an open-source language model by tokyotech-llm. Features: 13b LLM, VRAM: 26.2GB, Context: 4K, License: llama2, Instruction-Based, LLM Explorer Score: 0.11.

  Arxiv:2404.17733   Arxiv:2404.17790   Deploy:azure   En   Endpoints compatible   Instruct   Ja   Llama   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf 

Swallow 13B Instruct Hf Benchmarks

LLME Score: 0.10759
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Swallow 13B Instruct Hf (tokyotech-llm/Swallow-13b-instruct-hf)
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Swallow 13B Instruct Hf Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Limitations:
Models not tuned to ensure outputs align with human intent and safety considerations.
Additional Notes 
The models are continually pre-trained and instruction-tuned, emphasizing Japanese language capabilities.
Supported Languages 
supported_languages_list (Japanese, English), languages_details (The Swallow model has undergone continual pre-training with the addition of Japanese language data.)
Training Details 
Data Sources:
Japanese Wikipedia, RefinedWeb, Swallow Corpus, The Pile
Methodology:
Supervised fine-tuning (SFT) and instruction tuning using Anthropic HH-RLHF, Databricks Dolly 15-k, and OpenAssistant Conversations Dataset.
Model Architecture:
Refer to LLaMA-2 technical report for details on the model architecture.
Input Output 
Accepted Modalities:
text
Output Format:
Text
Performance Tips:
Model employs a tokenizer with a broadened vocabulary based on Japanese data, offering efficient text representation and faster inference.
Release Notes 
Version:
0.1
Date:
April 26, 2024
Notes:
Release of Swallow-7b-instruct-v0.1, Swallow-13b-instruct-v0.1, and Swallow-70b-instruct-v0.1.
Version:
N/A
Date:
March 2, 2024
Notes:
Release of Swallow-7b-plus-hf with twice as many Japanese tokens as Swallow-7b-hf.
Version:
N/A
Date:
February 4, 2024
Notes:
Release of Swallow-13b-NVE-hf.
Version:
N/A
Date:
January 26, 2024
Notes:
Release of Swallow-7b-NVE-hf, Swallow-7b-NVE-instruct-hf, Swallow-70b-NVE-hf, Swallow-70b-NVE-instruct-hf.
Version:
N/A
Date:
December 19, 2023
Notes:
Release of Swallow-7b-hf, Swallow-7b-instruct-hf, Swallow-13b-hf, Swallow-13b-instruct-hf, Swallow-70b-hf, Swallow-70b-instruct-hf.
LLM NameSwallow 13B Instruct Hf
Repository 🤗https://huggingface.co/tokyotech-llm/Swallow-13b-instruct-hf 
Model Size13b
Required VRAM26.2 GB
Updated2026-04-19
Maintainertokyotech-llm
Model Typellama
Instruction-BasedYes
Model Files  5.0 GB: 1-of-6   5.0 GB: 2-of-6   4.9 GB: 3-of-6   4.9 GB: 4-of-6   4.9 GB: 5-of-6   1.5 GB: 6-of-6
Supported Languagesen ja
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.33.2
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size43176
Torch Data Typebfloat16

Quantized Models of the Swallow 13B Instruct Hf

Model
Likes
Downloads
VRAM
Swallow 13B Instruct GGUF51865 GB
Swallow 13B Instruct AWQ1127 GB
Swallow 13B Instruct GPTQ287 GB

Best Alternatives to Swallow 13B Instruct Hf

Best Alternatives
Context / RAM
Downloads
Likes
NexusRaven V2 13B16K / 26 GB80470
CodeLlama 13B Instruct Hf16K / 26 GB5282159
CodeLlama 13B MORepair16K / 26 GB122
CodeLlama 13B Instruct Hf16K / 26 GB23427
TableLLM 13B16K / 26 GB30632
NexusRaven 13B16K / 26 GB17105
Panda Coder 13B16K / 26 GB2014
... Llama 2 13B Instruct Text2sql16K / 26 GB4228
Gen Sim16K / 0.3 GB83
Llama 3 13B Instruct Ft8K / 26.1 GB102
Note: green Score (e.g. "73.2") means that the model is better than tokyotech-llm/Swallow-13b-instruct-hf.

Rank the Swallow 13B Instruct Hf 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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