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Athene V2 Chat AWQ by radm

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  4-bit   Athene   Autotrain compatible   Awq Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-72b-in...   Chat model   Conversational   En   Endpoints compatible   Instruct   Nexusflow   Quantized   Qwen2   Region:us   Rlhf   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/radm/Athene-V2-Chat-AWQ 

Athene V2 Chat AWQ Benchmarks

LLME Score: 0.27906
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Athene V2 Chat AWQ (radm/Athene-V2-Chat-AWQ)

Athene V2 Chat AWQ Parameters and Internals

LLM NameAthene V2 Chat AWQ
Repository 🤗https://huggingface.co/radm/Athene-V2-Chat-AWQ 
Base Model(s)  Qwen/Qwen2.5-72B-Instruct   Qwen/Qwen2.5-72B-Instruct
Model Size72b
Required VRAM41.6 GB
Updated2024-12-22
Maintainerradm
Model Typeqwen2
Instruction-BasedYes
Model Files  5.0 GB: 1-of-9   4.9 GB: 2-of-9   5.0 GB: 3-of-9   5.0 GB: 4-of-9   4.9 GB: 5-of-9   4.9 GB: 6-of-9   4.9 GB: 7-of-9   4.5 GB: 8-of-9   2.5 GB: 9-of-9
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureQwen2ForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.42.4
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

Best Alternatives to Athene V2 Chat AWQ

Best Alternatives
Context / RAM
Downloads
Likes
Qwen2.5 72B Instruct AWQ32K / 41.6 GB5107851
Qwen2 72B Instruct AWQ32K / 41.6 GB1713537
Qwen2 72B Instruct AWQ GEMM32K / 41.6 GB120
Typhoon V1.5 72B Instruct AWQ32K / 41.3 GB283
ECE ILAB Q132K / 77.8 GB26010
Qwen2.5 72B Instruct 4bit32K / 40.9 GB178373
Qwen2.5 72B Instruct Bnb 4bit32K / 41.4 GB69853
Athene V2 Chat 4.65bpw H6 EXL232K / 44.3 GB1515
Qwen2.5 72B Instruct 8bit32K / 77.1 GB1134
Qwen2 72B Instruct Bnb 4bit32K / 41.2 GB3657
Note: green Score (e.g. "73.2") means that the model is better than radm/Athene-V2-Chat-AWQ.

Rank the Athene V2 Chat 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217
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