Update README.md

.gitignore

@@ -199,3 +199,4 @@ assets/wheels/vllm*.whl
 # DCP artifacts
 model_state_dict/
 forge_dcp_tmp/
+demo_top_down.md

README.md

@@ -1,10 +1,10 @@
 # <img width="35" height="35" alt="image" src="https://github.com/user-attachments/assets/2700a971-e5d6-4036-b03f-2f89c9791609" /> Forge
 
 
-#### A PyTorch native agentic library for RL post-training and agentic development
+#### A PyTorch native agentic library for RL post-training and agentic development that lets you focus on algorithms instead of writing infra code.
 
 ## Overview
-Forge was built with one core principle in mind: researchers should write algorithms, not infrastructure. Forge introduces a “service”-centric architecture that provides the right abstractions for distributed complexity. When you need fine-grained control over placement, fault handling or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
+The primary purpose of the Forge ecosystem is to delineate infra concerns from model concerns thereby making RL experimentation easier. Forge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
 
 Key features:
 - Usability for rapid research (isolating the RL loop from infrastructure)
@@ -18,15 +18,19 @@ Key features:
 > work. It's recommended that you signal your intention to contribute in the
 > issue tracker, either by filing a new issue or by claiming an existing one.
 
-## 📖 Documentation
+## 📖 Documentation (Coming Soon)
 
-View Forge's hosted documentation [at this link](https://meta-pytorch.org/forge/).
+View Forge's hosted documentation (coming soon)
+
+## Tutorials
+
+You can also find our notebook tutorials (coming soon)
 
 ## Installation
 
 ### Basic
 
-Forge requires the latest PyTorch nightly with Monarch, vLLM, and torchtitan. For convenience,
+Forge requires the latest PyTorch nightly with [Monarch](https://github.com/meta-pytorch/monarch), [vLLM](https://github.com/vllm-project/vllm), and [torchtitan](https://github.com/pytorch/torchtitan). For convenience,
 we have pre-packaged these dependencies as wheels in assets/wheels. (Note that the basic install script
 uses [DNF](https://docs.fedoraproject.org/en-US/quick-docs/dnf/), but could be easily extended to other Linux OS.)
 
@@ -40,7 +44,7 @@ conda activate forge
 
 Optional: By default, the packages installation uses conda. If user wants to install system packages on the target machine instead of conda, they can pass the `--use-sudo` to the installation script: `./script/install.sh --use-sudo`.
 
-After install, you can run the following command and should see output confirming GRPO training is running (you need a minimum 3 GPU devices).
+After install, you can run the following command and should see output confirming GRPO training is running (you need a minimum 3 GPU devices):
 
 ```
 python -m apps.grpo.main  --config apps/grpo/qwen3_1_7b.yaml
@@ -56,48 +60,6 @@ For your information, since the vLLM wheel is too large for GitHub, we uploaded
 $ gh release create v0.0.0 assets/wheels/vllm-*.whl --title "Forge Wheels v0.0.0"
 ```
 
-### Meta Internal Build (Alternative Route)
-
-1. Build uv package
-
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-git clone https://github.com/pytorch-labs/forge
-cd forge
-uv sync --all-extras
-source .venv/bin/activate
-```
-
-2. Setup CUDA on local machine
-
-```bash
-# feature install if you don't have /user/local/cuda-12.8
-feature install --persist cuda_12_9
-
-# add env variables
-export CUDA_VERSION=12.9
-export NVCC=/usr/local/cuda-$CUDA_VERSION/bin/nvcc
-export CUDA_NVCC_EXECUTABLE=/usr/local/cuda-$CUDA_VERSION/bin/nvcc
-export CUDA_HOME=/usr/local/cuda-$CUDA_VERSION
-export PATH="$CUDA_HOME/bin:$PATH"
-export CUDA_INCLUDE_DIRS=$CUDA_HOME/include
-export CUDA_CUDART_LIBRARY=$CUDA_HOME/lib64/libcudart.so
-export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
-```
-
-3. Build vllm from source
-
-```bash
-git clone https://github.com/vllm-project/vllm.git --branch v0.10.0
-cd vllm
-python use_existing_torch.py
-uv pip install -r requirements/build.txt
-uv pip install --no-build-isolation -e .
-```
-
-> [!WARNING]
-> If you add packages to the pyproject.toml, use `uv sync --inexact` so it doesn't remove Monarch and vLLM
-
 ## Quick Start
 
 To run SFT for Llama3 8B, run