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Cat Llama 3 70B AWQ Q256 W4 Gemm by catid

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  4-bit   Autotrain compatible   Awq   Conversational   Endpoints compatible   Instruct   Llama   Q256   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/catid/cat-llama-3-70b-awq-q256-w4-gemm 

Cat Llama 3 70B AWQ Q256 W4 Gemm Benchmarks

LLME Score: 0.15054
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Cat Llama 3 70B AWQ Q256 W4 Gemm (catid/cat-llama-3-70b-awq-q256-w4-gemm)
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Cat Llama 3 70B AWQ Q256 W4 Gemm Parameters and Internals

Additional Notes 
The model is quantized to ~4 bits per parameter using AutoAWQ.
LLM NameCat Llama 3 70B AWQ Q256 W4 Gemm
Repository 🤗https://huggingface.co/catid/cat-llama-3-70b-awq-q256-w4-gemm 
Model Size70b
Required VRAM39 GB
Updated2025-09-18
Maintainercatid
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB: 1-of-8   4.9 GB: 2-of-8   5.0 GB: 3-of-8   4.9 GB: 4-of-8   4.9 GB: 5-of-8   5.0 GB: 6-of-8   4.9 GB: 7-of-8   4.5 GB: 8-of-8
AWQ QuantizationYes
Quantization Typeawq|q256
Model ArchitectureLlamaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.38.2
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than catid/cat-llama-3-70b-awq-q256-w4-gemm.

Rank the Cat Llama 3 70B AWQ Q256 W4 Gemm Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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