Feat/curr adj (#394)

training/README.md

@@ -17,21 +17,22 @@ pip install -e .
 ```bash
 pip install ray wandb
 pip install torch==2.6.0
-pip install flash-attn --no-build-isolation
 ```
 
 4. Install veRL (tested with HEAD c34206925e2a50fd452e474db857b4d488f8602d):
 
 ```bash
-git clone https://github.com/volcengine/verl.git
-cd verl
-pip install -e .
+pip install git+https://github.com/volcengine/verl.git@c6dc8b73cf011aa75b8c6a47b0322f50aed800ad#egg=verl
 ```
 
 5. Install vLLM:
 
 ```bash
-pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
+pip install vllm==0.6.3 transformers==4.50.3 fire==0.7.0
+```
+6. Install flash attention
+```
+pip install flash-attn --no-build-isolation
 ```
 
 6. Log in to HF and W&B:
@@ -64,3 +65,33 @@ CUDA_VISIBLE_DEVICES=0,1 bash train.sh
 CUDA_VISIBLE_DEVICES is set to 0,1 to use the first two GPUs on the machine (see `nvidia-smi` output). This can be adjusted as needed. `tensor_model_parallel_size` and `n_gpus_per_node` should also be set to the number of GPUs you are using.
 
 You can change all configuration options by either modifying the config YAML (in this case, `config/llama3.1_1b_grpo.yaml`) or providing them as arguments to the Python script. Note that the batch sizes set in the Llama 1B and Qwen 1.5B configs are as high as it was possible for me to set them for the puzzles dataset mix on 2xA6000 GPUs without OOMs. Depending on the hardware you use and the datasets you train on, you may need to adjust these.
+
+
+# Exporting from FSDP checkpoint to HF model checkpoint
+
+After training your model the weights are saved across as a sharded checkpoints across several files. To faciliate simple evaluation of your trained model you may want to convert this into a HF model checkpoint. We have added a utility script to convert your sharded checkpoint into a hf checkpoint.
+
+To run this script. Navigate to the training directory and run the following
+
+```python
+python load_fsdp_to_hf.py /path/to/fsdp/checkpoint/global_step_num/actor /path/to/hugginface/checkpoint/global_step_num/actor/huggingface saved_model_name
+```
+
+For example
+
+```python
+python utils/load_fsdp_to_hf.py checkpoints/rg-test/intra_reasoning_algorithmic_qwen_3b_composite/global_step_400/actor/ checkpoints/rg-test/intra_reasoning_algorithmic_qwen_3b_composite/global_step_400/actor/huggingface qwen3b
+```
+
+# Run evaluations
+From here you may to run evaluations of your trained model. In the `training/evaluation` directory there is a script `evaluate_model.py` which you csn run to evaluate your trained model on a specific dataset. You specify evaluation parameters in a yaml file. This evaluation can point to either a local or remote model. For example the configuration file `training/evaluation/eval_algorithmic_composite.yaml` specifies the path to a local model which is stored as a hugginface checkpoint at `training/utils/qwen3b_500` (note that you have to convert to fsdp checkpoint to hf checkpoint for evaluation script to work as shown in the previous step).
+
+## Run the script
+Navigate to evaluations directory:
+```
+python evaluate_model.py --config path-to-yaml
+```
+For example
+```
+python evaluate_model.py --config eval_algorithmic_composite.yaml
+```