Upload folder using huggingface_hub

scripts/download.sh

@@ -0,0 +1,14 @@
+export TMPDIR=/pfss/mlde/workspaces/mlde_wsp_MGPATH/phuc/tmp
+export PIP_CACHE_DIR=/pfss/mlde/workspaces/mlde_wsp_MGPATH/phuc/.cache/pip
+mkdir -p "$TMPDIR" "$PIP_CACHE_DIR"
+
+# source ~/.bashrc
+# conda activate pre_rlvr
+# # cd verl/
+# # pip install -e .
+# pip install lmdeploy==0.15.0
+# pip install deepspeed==0.16.4
+# # Official Dao-AILab linux_x86_64 wheels need GLIBC 2.32 (Ubuntu 22.04).
+# # This host is Ubuntu 20.04 / GLIBC 2.31, so use Astral's manylinux_2_24 build.
+# pip install --no-deps \
+#   "https://wheels.astral.sh/artifacts/d4ffd81f93ca34e2e3f8b93499cd62e26faed24250657e7efcc3afd5a9b479df/flash_attn-2.8.3+cu.12.8.torch.2.8-cp311-cp311-manylinux_2_24_x86_64.whl"

scripts/run_dlms.sh

@@ -0,0 +1,62 @@
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT}"
+
+export TMPDIR=/pfss/mlde/workspaces/mlde_wsp_MGPATH/phuc/tmp
+export PIP_CACHE_DIR=/pfss/mlde/workspaces/mlde_wsp_MGPATH/phuc/.cache/pip
+mkdir -p "$TMPDIR" "$PIP_CACHE_DIR"
+
+CACHE_ROOT="${ROOT}/.cache"
+export HF_HOME="${CACHE_ROOT}/huggingface"
+export HUGGINGFACE_HUB_CACHE="${HF_HOME}/hub"
+export HF_DATASETS_CACHE="${HF_HOME}/datasets"
+export TRANSFORMERS_CACHE="${HF_HOME}/hub"
+export VLLM_CACHE_ROOT="${CACHE_ROOT}/vllm"
+export FLASHINFER_CACHE_DIR="${CACHE_ROOT}/flashinfer"
+export FLASHINFER_JIT_DIR="${CACHE_ROOT}/flashinfer/jit"
+export TRITON_CACHE_DIR="${CACHE_ROOT}/triton"
+export TORCHINDUCTOR_CACHE_DIR="${CACHE_ROOT}/torchinductor"
+export XDG_CACHE_HOME="${CACHE_ROOT}"
+mkdir -p "${HUGGINGFACE_HUB_CACHE}" "${HF_DATASETS_CACHE}" "${VLLM_CACHE_ROOT}" \
+  "${FLASHINFER_JIT_DIR}" "${TRITON_CACHE_DIR}" "${TORCHINDUCTOR_CACHE_DIR}"
+
+# DeepSpeed imports torch.utils.cpp_extension and requires CUDA_HOME/nvcc.
+# Same pattern as rubric_med: conda cuda-nvcc 12.8 + CUDA_HOME=$CONDA_PREFIX.
+if [ -z "${CUDA_HOME:-}" ] && [ -n "${CONDA_PREFIX:-}" ] && [ -x "${CONDA_PREFIX}/bin/nvcc" ]; then
+  export CUDA_HOME="${CONDA_PREFIX}"
+  export PATH="${CUDA_HOME}/bin:${PATH}"
+fi
+
+# source ~/.bashrc
+# conda activate pre_rlvr
+# # cd verl/
+# # pip install -e .
+# pip install lmdeploy==0.15.0
+# pip install deepspeed==0.16.4
+# # Official Dao-AILab linux_x86_64 wheels need GLIBC 2.32 (Ubuntu 22.04).
+# # This host is Ubuntu 20.04 / GLIBC 2.31, so use Astral's manylinux_2_24 build.
+# pip install --no-deps \
+#   "https://wheels.astral.sh/artifacts/d4ffd81f93ca34e2e3f8b93499cd62e26faed24250657e7efcc3afd5a9b479df/flash_attn-2.8.3+cu.12.8.torch.2.8-cp311-cp311-manylinux_2_24_x86_64.whl"
+op_ranges=("9-12")
+epochs=(1 4)
+block_sizes=(8 16)
+
+for epoch in "${epochs[@]}"; do
+for op_range in "${op_ranges[@]}"; do
+for block_size in "${block_sizes[@]}"; do
+
+deepspeed --num_gpus=4 src/run_dlms.py \
+    --model_path models/dLMs-block${block_size}-epoch${epoch}\
+    --dataset composition \
+    --learning_rate 1e-6 \
+    --model_name SDAR-100M-block${block_size}-epoch${epoch} \
+    --num_iterations 200 \
+    --mini_batch_size 1536\
+    --clip_ratio_high 0.28 \
+    --per_device_batch_size 64 \
+    --num_samples 1024 \
+    --num_generations_per_sample 6 \
+    --op_range $op_range
+
+done
+done
+done

scripts/run_dlms_context.sh

@@ -0,0 +1,65 @@
+ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${ROOT}"
+
+export TMPDIR=/pfss/mlde/workspaces/mlde_wsp_MGPATH/phuc/tmp
+export PIP_CACHE_DIR=/pfss/mlde/workspaces/mlde_wsp_MGPATH/phuc/.cache/pip
+mkdir -p "$TMPDIR" "$PIP_CACHE_DIR"
+
+CACHE_ROOT="${ROOT}/.cache"
+export HF_HOME="${CACHE_ROOT}/huggingface"
+export HUGGINGFACE_HUB_CACHE="${HF_HOME}/hub"
+export HF_DATASETS_CACHE="${HF_HOME}/datasets"
+export TRANSFORMERS_CACHE="${HF_HOME}/hub"
+export VLLM_CACHE_ROOT="${CACHE_ROOT}/vllm"
+export FLASHINFER_CACHE_DIR="${CACHE_ROOT}/flashinfer"
+export FLASHINFER_JIT_DIR="${CACHE_ROOT}/flashinfer/jit"
+export TRITON_CACHE_DIR="${CACHE_ROOT}/triton"
+export TORCHINDUCTOR_CACHE_DIR="${CACHE_ROOT}/torchinductor"
+export XDG_CACHE_HOME="${CACHE_ROOT}"
+mkdir -p "${HUGGINGFACE_HUB_CACHE}" "${HF_DATASETS_CACHE}" "${VLLM_CACHE_ROOT}" \
+  "${FLASHINFER_JIT_DIR}" "${TRITON_CACHE_DIR}" "${TORCHINDUCTOR_CACHE_DIR}"
+
+# DeepSpeed imports torch.utils.cpp_extension and requires CUDA_HOME/nvcc.
+# Same pattern as rubric_med: conda cuda-nvcc 12.8 + CUDA_HOME=$CONDA_PREFIX.
+if [ -z "${CUDA_HOME:-}" ] && [ -n "${CONDA_PREFIX:-}" ] && [ -x "${CONDA_PREFIX}/bin/nvcc" ]; then
+  export CUDA_HOME="${CONDA_PREFIX}"
+  export PATH="${CUDA_HOME}/bin:${PATH}"
+fi
+
+# source ~/.bashrc
+# conda activate pre_rlvr
+# # cd verl/
+# # pip install -e .
+# pip install lmdeploy==0.15.0
+# pip install deepspeed==0.16.4
+# # Official Dao-AILab linux_x86_64 wheels need GLIBC 2.32 (Ubuntu 22.04).
+# # This host is Ubuntu 20.04 / GLIBC 2.31, so use Astral's manylinux_2_24 build.
+# pip install --no-deps \
+#   "https://wheels.astral.sh/artifacts/d4ffd81f93ca34e2e3f8b93499cd62e26faed24250657e7efcc3afd5a9b479df/flash_attn-2.8.3+cu.12.8.torch.2.8-cp311-cp311-manylinux_2_24_x86_64.whl"
+
+
+deepspeed --num_gpus=4 src/run_dlms.py \
+    --model_path models/dLMs-0.999zoo_op2-20+0.001teacher_op2 \
+    --dataset context \
+    --learning_rate 1e-6 \
+    --model_name SDAR-100M-0.999zoo_op2-20+0.001teacher_op2-process \
+    --num_iterations 200 \
+    --mini_batch_size 1536\
+    --clip_ratio_high 0.28 \
+    --per_device_batch_size 64 \
+    --use_step_process_reward \
+    --num_samples 1024 \
+    --num_generations_per_sample 6 \
+
+deepspeed --num_gpus=4 src/run_ar.py \
+    --model_path models/AR-0.999zoo_op2-20+0.001teacher_op2 \
+    --dataset context\
+    --learning_rate 1e-6 \
+    --model_name Qwen3-100M-AR-0.999zoo_op2-20+0.001teacher_op2-process\
+    --num_iterations 200 \
+    --mini_batch_size 1536\
+    --clip_ratio_high 0.28 \
+    --per_device_batch_size 64 \
+    --use_step_process_reward \
+    --num_samples 1024 \
+    --num_generations_per_sample 6 \