Update README.md

Louis24 · Nov 12, 2023 · 3f108a7 · 3f108a7
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 | 2022-01 |         COT         |      Google      | [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/pdf/2201.11903.pdf)                                                                          |   NeurIPS<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F1b6e810ce0afd0dd093f789d2b2742d047e316d5%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2022-01 |        LaMDA        |      Google      | [LaMDA: Language Models for Dialog Applications](https://arxiv.org/pdf/2201.08239.pdf)                                                                                                 |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fb3848d32f7294ec708627897833c4097eb4d8778%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2022-01 |        Minerva      |      Google      | [Solving Quantitative Reasoning Problems with Language Models](https://arxiv.org/abs/2206.14858)                                                                                                 |   NeurIPS<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fab0e3d3e4d42369de5933a3b4c237780b41c0d77%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)     |
-| 2022-01 | Megatron-Turing NLG | Microsoft&NVIDIA | [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/pdf/2201.11990.pdf)                                        | ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7cbc2a7843411a1768ab762930707af0a3c33a19%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
+| 2022-01 | Megatron-Turing NLG | Microsoft&NVIDIA | [Using Deep and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/pdf/2201.11990.pdf)                                        | ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7cbc2a7843411a1768ab762930707af0a3c33a19%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2022-03 |     InstructGPT     |      OpenAI      | [Training language models to follow instructions with human feedback](https://arxiv.org/pdf/2203.02155.pdf)                                                                            |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fd766bffc357127e0dc86dd69561d5aeb520d6f4c%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2022-04 |         PaLM         |      Google      | [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/pdf/2204.02311.pdf)                                                                                                  |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F094ff971d6a8b8ff870946c9b3ce5aa173617bfb%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
 | 2022-04 |      Chinchilla      |     DeepMind     | [An empirical analysis of compute-optimal large language model training](https://www.deepmind.com/publications/an-empirical-analysis-of-compute-optimal-large-language-model-training) |   NeurIPS<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fbb0656031cb17adf6bac5fd0fe8d53dd9c291508%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)  |
@@ -306,10 +306,10 @@ The above tables coule be better summarized by this wonderful visualization from
 - [CometLLM](https://github.com/comet-ml/comet-llm) - A 100% opensource LLMOps platform to log, manage, and visualize your LLM prompts and chains. Track prompt templates, prompt variables, prompt duration, token usage, and other metadata. Score prompt outputs and visualize chat history all within a single UI.
 - [IntelliServer](https://github.com/intelligentnode/IntelliServer) - simplifies the evaluation of LLMs by providing a unified microservice to access and test multiple AI models.
 - [OpenLLM](https://github.com/bentoml/OpenLLM) - Fine-tune, serve, deploy, and monitor any open-source LLMs in production. Used in production at [BentoML](https://bentoml.com/) for LLMs-based applications.
-- [DeepSpeed-Mii] -  MII makes low-latency and high-throughput inference, similar to vLLM powered by DeepSpeed.
+- [DeepSpeed-Mii](https://github.com/microsoft/DeepSpeed-MII) -  MII makes low-latency and high-throughput inference, similar to vLLM powered by DeepSpeed.
 - [Text-Embeddings-Inference](https://github.com/huggingface/text-embeddings-inference) - Inference for text-embeddings in Rust, HFOIL Licence.
 - [Infinity](https://github.com/michaelfeil/infinity) - Inference for text-embeddings in Python
-
+- [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) - Nvidia Framework for LLM Inference
 ## Prompting libraries & tools
 
 - [YiVal](https://github.com/YiVal/YiVal) — Evaluate and Evolve: YiVal is an open-source GenAI-Ops tool for tuning and evaluating prompts, configurations, and model parameters using customizable datasets, evaluation methods, and improvement strategies.