Add collaborativeagents module and update gitignore

- Add collaborativeagents subproject with adapters, agents, and evaluation modules
- Update .gitignore to exclude large binary files (.whl, .tar), wandb logs, and results

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

1 files changed, 57 insertions, 0 deletions

diff --git a/collaborativeagents/training/train_sft.sbatch b/collaborativeagents/training/train_sft.sbatch
new file mode 100644
index 0000000..a60894d
--- /dev/null
+++ b/collaborativeagents/training/train_sft.sbatch
@@ -0,0 +1,57 @@
+#!/bin/bash
+#SBATCH --job-name=sft_train
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuH200x8
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=32
+#SBATCH --gres=gpu:4
+#SBATCH --mem=256G
+#SBATCH --time=08:00:00
+#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/sft_train_%j.out
+#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/sft_train_%j.err
+
+set -e
+cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents
+source /u/yurenh2/miniforge3/etc/profile.d/conda.sh
+conda activate eval
+
+export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface
+export PYTHONPATH="${PWD}:${PWD}/../src:${PYTHONPATH}"
+export WANDB_MODE=offline
+
+echo "=== SFT Training (H200) ==="
+date
+nvidia-smi --query-gpu=index,name,memory.total --format=csv
+
+MODEL_PATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
+DATA_PATH="training/training_data/sft_training_data.json"
+OUTPUT_DIR="training/outputs/sft_reflection"
+
+echo "Model: $MODEL_PATH"
+echo "Data: $DATA_PATH"
+echo "Output: $OUTPUT_DIR"
+
+# Count training examples
+echo ""
+echo "Training data size: $(wc -c < $DATA_PATH) bytes"
+python -c "import json; data = json.load(open('$DATA_PATH')); print(f'Training examples: {len(data)}')"
+
+echo ""
+echo "Starting SFT training..."
+
+# Training with 4 GPUs using FSDP for memory efficiency
+accelerate launch --num_processes=4 --mixed_precision=bf16 \
+    training/train_sft.py \
+    --model-path $MODEL_PATH \
+    --data-path $DATA_PATH \
+    --output-dir $OUTPUT_DIR \
+    --num-epochs 4 \
+    --learning-rate 1e-6 \
+    --batch-size 1 \
+    --gradient-accumulation 16
+
+echo ""
+echo "Training complete!"
+date
+ls -la $OUTPUT_DIR/