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Matsu 7B by Manual-Dataset-Creation-Project

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  Autotrain compatible Base model:finetune:qwen/qwen2...   Base model:qwen/qwen2.5-7b   Conversational   Dataset:llm-jp/oasst2-33k-ja Dataset:manual-dataset-creatio...   Ja   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Manual-Dataset-Creation-Project/Matsu-7B 

Matsu 7B Benchmarks

LLME Score: 0.28692
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Matsu 7B (Manual-Dataset-Creation-Project/Matsu-7B)

Matsu 7B Parameters and Internals

LLM NameMatsu 7B
Repository 🤗https://huggingface.co/Manual-Dataset-Creation-Project/Matsu-7B 
Base Model(s)  Qwen/Qwen2.5-7B   Qwen/Qwen2.5-7B
Model Size7b
Required VRAM15.2 GB
Updated2025-01-17
MaintainerManual-Dataset-Creation-Project
Model Typeqwen2
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   4.3 GB: 3-of-4   1.1 GB: 4-of-4
Supported Languagesja
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.48.0.dev0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typebfloat16
Errorsreplace

Best Alternatives to Matsu 7B

Best Alternatives
Context / RAM
Downloads
Likes
Qwen2.5 7B128K / 15.4 GB9462798
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Light 7B Beta128K / 15.2 GB141
Teleut 7B128K / 15.2 GB30811
Gte Qwen2 7B Instruct128K / 30.5 GB43117244
SakalFusion 7B Alpha128K / 15.2 GB130
Rombos LLM V2.5 Qwen 7B128K / 15.2 GB74515
Lexora Medium 7B128K / 15.2 GB51575
SeaLLMs V3 7B Chat128K / 15.2 GB1848748
Rombo LLM V2.5 Qwen 7B128K / 15.2 GB171
Note: green Score (e.g. "73.2") means that the model is better than Manual-Dataset-Creation-Project/Matsu-7B.

Rank the Matsu 7B 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 v20241227
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