Update pyproject.toml + tools Falcon 3 addition

.tool-versions

@@ -1,2 +1,2 @@
 python 3.11.4
-poetry 1.6.1
+poetry 1.8.4

falcon/falcon3-10B-trt-llm-spec-dec/README.md

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+# Falcon 3 10B Instruct using TensorRT-LLM with Speculative Decoding
+
+This directory is [Truss](https://truss.baseten.co/) template for deploying model Llama 3.1 8B Instruct using our TensorRT-LLM (TRT-LLM) [engine builder](https://docs.baseten.co/performance/engine-builder-overview). This configuration is optimized for high-throughput scenarios.
+
+## Use case
+
+This deployment is tailored for applications that require processing large volumes of data with moderate to low latency, such as:
+* Content moderation for social media platforms
+* Bulk article summarization
+* Large-scale Retrieval-Augmented Generation (RAG) systems
+
+## Configuration
+
+The template uses the following key configuration parameters:
+
+| Property            | Value    | Description                                                                    |
+| ------------------- | -------- | ------------------------------------------------------------------------------ |
+| GPU                 | 1xH100   | Single NVIDIA H100 GPU                                                         |
+| `max_batch_size`    | 32       | Allows processing up to 32 requests simultaneously                             |
+| `quantization_type` | `fp8_kv` | FP8 quantization, balancing performance and accuracy. See this [blog](https://www.baseten.co/blog/33-faster-llm-inference-with-fp8-quantization/). |
+| `max_input_len`     | 4096     | Maximum number of input tokens that the model will accept                      |
+| `max_output_len`    | 1024     | Maximum number of output tokens the model can generate                         |
+
+
+## Deployment
+
+First, clone this repository:
+
+```sh
+git clone https://github.com/basetenlabs/truss-examples/
+cd falcon/falcon3-10B-trt-llm-spec-dec
+```
+
+Before deployment:
+
+1. Make sure you have a [Baseten account](https://app.baseten.co/signup) and [API key](https://app.baseten.co/settings/account/api_keys).
+2. Install the latest version of Truss: `pip install --upgrade truss`
+
+With `falcon/falcon3-10B-trt-llm-spec-dec` as your working directory, you can deploy the model with:
+
+```sh
+truss push --trusted --publish
+```
+
+Paste your Baseten API key if prompted. Also ensure the `hf_access_token` secret is properly setup in your Baseten Account to access this model.
+
+**Note**: TensorRT-LLM with engine builder will only work under a Baseten production deployment
+
+For more information, refer to the [Truss documentation](https://docs.baseten.co/performance/engine-builder-overview).