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Faro Yi 9B DPO by wenbopan

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  Merged Model   Arxiv:2303.08774   Autotrain compatible   Conversational Dataset:argilla/ultrafeedback-...   Dataset:intel/orca dpo pairs Dataset:jondurbin/truthy-dpo-v... Dataset:wenbopan/chinese-dpo-p...   En   Endpoints compatible   Llama   Region:us   Safetensors   Sharded   Tensorflow   Zh
Model Card on HF 🤗: https://huggingface.co/wenbopan/Faro-Yi-9B-DPO 

Faro Yi 9B DPO Benchmarks

ARC: 64.16 vs 96.7 (so35)-33.7%
HellaSwag: 78.92 vs 95.3 (gpt4)-17.2%
MMLU: 70.74 vs 88.3 (so35)-19.9%
TruthfulQA: 56.25 vs 59 (gpt4)-4.7%
WinoGrande: 77.82 vs 87.5 (gpt4)-11.1%
GSM8K: 64.75 vs 96.4 (so35)-32.8%
LLME Score: 0.25038
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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Faro Yi 9B DPO Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, commercial applications
Applications:
text generation, question answering
Primary Use Cases:
Chatbots, Virtual assistants
Additional Notes 
Supports 4bit-AWQ quantization to boost input length to 160K, with some performance impact.
Supported Languages 
en (proficient), zh (proficient)
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 with a max prompt of 32K.
LLM NameFaro Yi 9B DPO
Repository 🤗https://huggingface.co/wenbopan/Faro-Yi-9B-DPO 
Merged ModelYes
Model Size9b
Required VRAM17.7 GB
Updated2024-11-24
Maintainerwenbopan
Model Typellama
Model Files  10.0 GB: 1-of-2   7.7 GB: 2-of-2
Supported Languagesen zh
Model ArchitectureLlamaForCausalLM
Licensemit
Context Length32768
Model Max Length32768
Transformers Version4.39.3
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typebfloat16
Faro Yi 9B DPO (wenbopan/Faro-Yi-9B-DPO)

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

Rank the Faro Yi 9B DPO 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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