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readme updates, bump up version
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README.md

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## New in this release
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## 2.1.1
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1. Optimized prompt templates and config files for DeepSeek-R1 and Amazon Nova for `ConvFinQA` and `LongBench` datasets.
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## 2.1.0
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1. Deepseek-R1 distilled model support using [`vllm`](https://github.com/vllm-project/vllm).

docs/manifest.md

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[├── bedrock/config-llama-3-2-all-models.yml](configs/bedrock/config-llama-3-2-all-models.yml)
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[├── bedrock/config-llama-3-3-all-models-open-orca.yml](configs/bedrock/config-llama-3-3-all-models-open-orca.yml)
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[├── bedrock/config-llama-3-3-all-models.yml](configs/bedrock/config-llama-3-3-all-models.yml)
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[├── bedrock/config-nova-all-models-convfinqa.yml](configs/bedrock/config-nova-all-models-convfinqa.yml)
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[├── bedrock/config-nova-all-models-dolly-dataset.yml](configs/bedrock/config-nova-all-models-dolly-dataset.yml)
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[├── bedrock/config-nova-all-models-openarca.yml](configs/bedrock/config-nova-all-models-openarca.yml)
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[└── bedrock/config-nova-all-models.yml](configs/bedrock/config-nova-all-models.yml)

docs/releases.md

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# Releases
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## 2.1.1
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1. Optimized prompt tempaltes for DeepSeek-R1 and Amazon Nova for `ConvFinQA` and `LongBench` datasets.
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## 2.1.0
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1. Deepseek-R1 distilled model support using [`vllm`](https://github.com/vllm-project/vllm).

manifest.txt

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configs/bedrock/config-llama-3-2-all-models.yml
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configs/bedrock/config-llama-3-3-all-models-open-orca.yml
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configs/bedrock/config-llama-3-3-all-models.yml
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configs/bedrock/config-nova-all-models-convfinqa.yml
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configs/bedrock/config-nova-all-models-dolly-dataset.yml
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configs/bedrock/config-nova-all-models-openarca.yml
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configs/bedrock/config-nova-all-models.yml

pyproject.toml

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[project]
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name = "fmbench"
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version = "2.1.0"
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version = "2.1.1"
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description = "Benchmark performance of **any Foundation Model (FM)** deployed on **any AWS Generative AI service**, be it **Amazon SageMaker**, **Amazon Bedrock**, **Amazon EKS**, or **Amazon EC2**. The FMs could be deployed on these platforms either directly through `FMbench`, or, if they are already deployed then also they could be benchmarked through the **Bring your own endpoint** mode supported by `FMBench`."
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authors = [
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{ name = "Amit Arora", email = "[email protected]" },

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