Llama 3 Smaug 8B AWQ by solidrust

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  4-bit   Autotrain compatible   Awq Base model:abacusai/llama-3-sm... Base model:quantized:abacusai/...   Conversational Dataset:anon8231489123/sharegp...   Dataset:aqua rat Dataset:m-a-p/codefeedback-fil... Dataset:microsoft/orca-math-wo...   Endpoints compatible   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow

Llama 3 Smaug 8B AWQ Benchmarks

Llama 3 Smaug 8B AWQ (solidrust/Llama-3-Smaug-8B-AWQ)

Llama 3 Smaug 8B AWQ Parameters and Internals

Model Type 
text-generation
Additional Notes 
This model was built using the Smaug recipe for improving performance on real-world multi-turn conversations. AWQ is an efficient, accurate, and blazing-fast low-bit weight quantization method, supporting 4-bit quantization. AWQ models are currently supported on Linux and Windows with NVidia GPUs only. macOS users should use GGUF models instead.
LLM NameLlama 3 Smaug 8B AWQ
Repository ๐Ÿค—https://huggingface.co/solidrust/Llama-3-Smaug-8B-AWQ 
Base Model(s)  Llama 3 Smaug 8B   abacusai/Llama-3-Smaug-8B
Model Size8b
Required VRAM5.8 GB
Updated2024-12-22
Maintainersolidrust
Model Typellama
Model Files  4.7 GB: 1-of-2   1.1 GB: 2-of-2
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length8192
Model Max Length8192
Transformers Version4.38.2
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128256
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than solidrust/Llama-3-Smaug-8B-AWQ.

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241217