Faro Yi 9B by wenbopan

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  Merged Model   Arxiv:2303.08774   Autotrain compatible   Conversational   Dataset:wenbopan/fusang-v1 Dataset:wenbopan/openorca-zh-2...   En   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow   Zh
Model Card on HF ๐Ÿค—: https://huggingface.co/wenbopan/Faro-Yi-9B 

Faro Yi 9B Benchmarks

Faro Yi 9B (wenbopan/Faro-Yi-9B)
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Faro Yi 9B Parameters and Internals

Model Type 
chat, long-context modeling
Use Cases 
Areas:
practical applications, long-context modeling
Supported Languages 
english (high), chinese (high)
Input Output 
Input Format:
chatml template
Accepted Modalities:
text
Output Format:
text
Performance Tips:
For longer inputs under 24GB of VRAM, it is recommended to use vLLM to have a max prompt of 32K. Setting kv_cache_dtype="fp8_e5m2" allows for 48K input length. 4bit-AWQ quantization on top of that can boost input length to 160K.
LLM NameFaro Yi 9B
Repository ๐Ÿค—https://huggingface.co/wenbopan/Faro-Yi-9B 
Merged ModelYes
Model Size9b
Required VRAM17.7 GB
Updated2025-07-08
Maintainerwenbopan
Model Typellama
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.8 GB: 4-of-4
Supported Languageszh en
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.38.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than wenbopan/Faro-Yi-9B.

Rank the Faro Yi 9B 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 v20241124