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Phoenix AWQ by DRXD1000

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Phoenix AWQ is an open-source language model by DRXD1000. Features: 7.2b LLM, VRAM: 4.2GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.11.

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

Phoenix AWQ Benchmarks

LLME Score: 0.11037
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Phoenix AWQ (DRXD1000/Phoenix-AWQ)
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Phoenix AWQ Parameters and Internals

Model Type 
GPT-like, 7B model, DPO fine-tuned
Use Cases 
Areas:
research, commercial applications
Additional Notes 
Phoenix training procedure follows the process of alignment-handbook from Huggingface.
Supported Languages 
German (High proficiency)
Training Details 
Data Sources:
HuggingFaceH4/ultrachat_200k, argilla/ultrafeedback-binarized-preferences
Methodology:
Direct Preference Optimization (DPO)
Context Length:
4096
Hardware Used:
8 x A100 80GB
Model Architecture:
GPT-like
Input Output 
Input Format:
<|system|> ~~ <|user|> {prompt}~~ <|assistant|>
Accepted Modalities:
text
Output Format:
text
Performance Tips:
Avoid system prompts as Phoenix does not react well to them.
LLM NamePhoenix AWQ
Repository 🤗https://huggingface.co/DRXD1000/Phoenix-AWQ 
Model Size7.2b
Required VRAM4.2 GB
Updated2026-05-23
MaintainerDRXD1000
Model Typemistral
Model Files  4.2 GB
Supported Languagesde
AWQ QuantizationYes
Quantization Typeawq
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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Note: green Score (e.g. "73.2") means that the model is better than DRXD1000/Phoenix-AWQ.

Rank the Phoenix AWQ 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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