Deci
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@@ -131,7 +131,7 @@ DeciCoder was trained on the Python, Java, and Javascript subsets of [Starcoder
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  - **Warm-Up Steps**: 9000
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  - **Total Training Steps**: 284k
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- - **Total Tokenes**: 446B
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  - **Global Batch Size**: 768
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  - **Optimizer**: AdamW
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  - **Optimizer Parameters**: beta1=0.9, beta2=0.95
@@ -150,10 +150,10 @@ Below are DeciCoder's pass@1 on MultiPL HumanEval scores
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  ### Runtime Benchmarks
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- |Inference Tool/Hardware | A10 (tokens/sec) | A10 Latency (ms)| A100 (tokens/sec) | A100 Latency (ms) |
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- |:----------|:----------|:----------|:----------|:----------|
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- | HF Inference Endpoints | 1,364.2 | 9.03 | 3,244.4 | 8.8 |
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- | Infery LLM | 3,889.3 | 3.075 | 11,676.8 | 1.729 |
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  - Latency - Total generation time of batch size 1 (prefill+generate)
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  - Throughput (tokens/sec) - Measured with optimal batch size per hardware - A10 on BS 128, A100 on BS 512
 
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  - **Warm-Up Steps**: 9000
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  - **Total Training Steps**: 284k
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+ - **Total Tokens**: 446B
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  - **Global Batch Size**: 768
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  - **Optimizer**: AdamW
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  - **Optimizer Parameters**: beta1=0.9, beta2=0.95
 
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  ### Runtime Benchmarks
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+ |Inference Tool/Hardware | A10 (tokens/sec) |A100 (tokens/sec) |
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+ |:----------|:----------|:----------|
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+ | HF Inference Endpoints | 1,364.2 | 3,244.4 |
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+ | Infery LLM | 3,889.3 | 11,676.8 |
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  - Latency - Total generation time of batch size 1 (prefill+generate)
159
  - Throughput (tokens/sec) - Measured with optimal batch size per hardware - A10 on BS 128, A100 on BS 512