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Phoenix GPTQ by DRXD1000

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  Arxiv:2401.10580   4-bit   4bit   Alignment-handbook   Autotrain compatible   Conversational   De   Dpo   Endpoints compatible   Gptq   Mistral   Quantization   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/DRXD1000/Phoenix-GPTQ 

Phoenix GPTQ Benchmarks

LLME Score: 0.15488
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Sponsored by Nebius

Phoenix GPTQ Parameters and Internals

Model Type 
GPT-like 7B model DPO fine-tuned
Additional Notes 
Model trained in German with GPTQ quantization.
Supported Languages 
German (NLP)
Training Details 
Data Sources:
HuggingFaceH4/ultrachat_200k, argilla/ultrafeedback-binarized-preferences
Hardware Used:
8 x A100 80GB
Input Output 
Input Format:
<|system|> ~~ <|user|> {prompt}~~ <|assistant|>
Accepted Modalities:
text
LLM NamePhoenix GPTQ
Repository 🤗https://huggingface.co/DRXD1000/Phoenix-GPTQ 
Model Size1.2b
Required VRAM4.2 GB
Updated2025-03-12
MaintainerDRXD1000
Model Typemistral
Model Files  4.2 GB
Supported Languagesde
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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Mistral Ft Optimized 1227 GPTQ32K / 4.2 GB372
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...hat 3.5 1210 Seraph Slerp GPTQ32K / 4.2 GB92
Note: green Score (e.g. "73.2") means that the model is better than DRXD1000/Phoenix-GPTQ.

Rank the Phoenix GPTQ Capabilities

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Instruction Following and Task Automation  
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Factuality and Completeness of Knowledge  
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Censorship and Alignment  
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Data Analysis and Insight Generation  
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Text Generation  
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Text Summarization and Feature Extraction  
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Code Generation  
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Multi-Language Support and Translation  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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