diff --git a/frameworks/transformers/5.5.0/Dockerfile b/frameworks/transformers/5.5.0/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..44701f4037f15104c81f4cde499cfad89fe98a2d
--- /dev/null
+++ b/frameworks/transformers/5.5.0/Dockerfile
@@ -0,0 +1,20 @@
+
+FROM opencloudos/opencloudos9-cuda-devel:12.8
+
+LABEL maintainer="stronking 363133710@qq.com"
+LABEL org.opencontainers.image.source="https://gitee.com/OpenCloudOS/ai-agent-container"
+LABEL org.opencontainers.image.description="Torch 2.10.0 (GPU) on OpenCloudOS 9"
+ENV NVIDIA_VISIBLE_DEVICES=all
+
+ENV NVIDIA_VISIBLE_DEVICES=all \
+    PYTHONUNBUFFERED=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PIP_DEFAULT_TIMEOUT=120
+
+
+WORKDIR /home
+
+RUN --mount=type=cache,id=pip-cache-opencloudos9-cu128,target=/root/.cache/pip \
+    pip install "transformers[torch] @ git+https://github.com/huggingface/transformers.git@v5.5.0"
+
+RUN ["python3"]
\ No newline at end of file
diff --git a/frameworks/transformers/5.5.0/README.md b/frameworks/transformers/5.5.0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..86f19f6ee58d85afc4df1964fddd637d4f757cee
--- /dev/null
+++ b/frameworks/transformers/5.5.0/README.md
@@ -0,0 +1,20 @@
+# Transformers 5.5.0 on OpenCloudOS 9
+
+## 基本信息
+- **框架版本**：v5.5.0
+- **基础镜像**：opencloudos9-cuda-devel:12.8
+- **Python 版本**：3.11
+- **CUDA 版本**： 12.x 或 更高
+
+## 构建
+
+docker build -t oc9-transformers:5.5.0 .
+
+## 镜像启动命令
+
+docker run -d --gpus all --name oc9-transformers oc9-transformers:5.5.0
+
+## 镜像测试命令
+
+docker run --rm --gpus all oc9-transformers:5.5.0 bash test.sh
+
diff --git a/frameworks/transformers/5.5.0/build.conf b/frameworks/transformers/5.5.0/build.conf
new file mode 100644
index 0000000000000000000000000000000000000000..347fa33f2090cfa71e8b61669fe582c3faf89527
--- /dev/null
+++ b/frameworks/transformers/5.5.0/build.conf
@@ -0,0 +1,4 @@
+# transformers 5.5.0 on OpenCloudOS 9 (GPU)
+IMAGE_NAME=oc9-transformers
+IMAGE_TAG=5.5.0
+GPU_TEST=true
\ No newline at end of file
diff --git a/frameworks/transformers/5.5.0/test.sh b/frameworks/transformers/5.5.0/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..14a9b3cca48726a87a47177059c1c50aeccb6abe
--- /dev/null
+++ b/frameworks/transformers/5.5.0/test.sh
@@ -0,0 +1,233 @@
+#!/usr/bin bash
+# 在容器外执行：验证指定 Docker 镜像中的 transformers / torch / CUDA 基础功能。
+# 用法：bash test_transformers_docker.sh <image:tag> [额外 docker run 参数...]
+# 示例：bash test_transformers_docker.sh my-transformers:latest
+# 示例：REQUIRE_CUDA=0 bash test_transformers_docker.sh my-transformers:latest
+# 示例：DOCKER_NETWORK=bridge bash test_transformers_docker.sh my-transformers:latest
+
+set -Eeuo pipefail
+
+IMAGE="${1:-}"
+if [[ -z "${IMAGE}" || "${IMAGE}" == "-h" || "${IMAGE}" == "--help" ]]; then
+  cat <<'USAGE'
+用法:
+  bash test_transformers_docker.sh <image:tag> [额外 docker run 参数...]
+
+环境变量:
+  REQUIRE_CUDA=1|0          是否强制要求 CUDA/GPU 可用，默认 1
+  GPUS=all                  传给 docker run --gpus 的值，默认 all
+  PYTHON_BIN=python3        容器内 Python 命令，默认 python3
+  DOCKER_NETWORK=none       docker 网络模式，默认 none，避免测试时联网下载模型
+  TIMEOUT_SECONDS=180       整体测试超时时间，默认 180 秒
+
+示例:
+  bash test_transformers_docker.sh registry.example.com/ai/transformers:cu121
+  REQUIRE_CUDA=0 bash test_transformers_docker.sh transformers-cpu:test
+  DOCKER_NETWORK=bridge bash test_transformers_docker.sh my-image:tag --ipc=host
+USAGE
+  exit 1
+fi
+shift || true
+
+REQUIRE_CUDA="${REQUIRE_CUDA:-1}"
+GPUS="${GPUS:-all}"
+PYTHON_BIN="${PYTHON_BIN:-python3}"
+DOCKER_NETWORK="${DOCKER_NETWORK:-none}"
+TIMEOUT_SECONDS="${TIMEOUT_SECONDS:-180}"
+EXTRA_DOCKER_ARGS=("$@")
+
+log()  { printf '\033[1;34m%s\033[0m\n' "$*"; }
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+
+[[ "${REQUIRE_CUDA}" =~ ^[01]$ ]] || fail "REQUIRE_CUDA 只能是 1 或 0，当前值: ${REQUIRE_CUDA}"
+
+command -v docker >/dev/null 2>&1 || fail "未找到 docker 命令"
+docker info >/dev/null 2>&1 || fail "docker daemon 不可用，请确认 Docker 服务已启动且当前用户有权限访问"
+
+if ! docker image inspect "${IMAGE}" >/dev/null 2>&1; then
+  warn "本地未找到镜像 ${IMAGE}；docker run 可能会尝试拉取镜像"
+fi
+
+DOCKER_ARGS=(run --rm -i)
+if [[ "${REQUIRE_CUDA}" == "1" ]]; then
+  DOCKER_ARGS+=(--gpus "${GPUS}")
+fi
+DOCKER_ARGS+=(
+  --network "${DOCKER_NETWORK}"
+  -e "REQUIRE_CUDA=${REQUIRE_CUDA}"
+  -e "PYTHON_BIN=${PYTHON_BIN}"
+  -e "TRANSFORMERS_OFFLINE=1"
+  -e "HF_HUB_OFFLINE=1"
+  -e "HF_HOME=/tmp/hf-home"
+)
+DOCKER_ARGS+=("${EXTRA_DOCKER_ARGS[@]}")
+DOCKER_ARGS+=("${IMAGE}" /bin/bash -s)
+
+log "=== Transformers Docker 镜像功能测试 ==="
+printf '镜像: %s\n' "${IMAGE}"
+printf 'CUDA 强制检查: %s\n' "${REQUIRE_CUDA}"
+printf 'Docker 网络: %s\n' "${DOCKER_NETWORK}"
+printf 'Python 命令: %s\n' "${PYTHON_BIN}"
+if ((${#EXTRA_DOCKER_ARGS[@]} > 0)); then
+  printf '额外 docker 参数: %s\n' "${EXTRA_DOCKER_ARGS[*]}"
+fi
+
+RUN_CMD=(docker "${DOCKER_ARGS[@]}")
+if command -v timeout >/dev/null 2>&1; then
+  RUN_CMD=(timeout --preserve-status "${TIMEOUT_SECONDS}s" "${RUN_CMD[@]}")
+fi
+
+"${RUN_CMD[@]}" <<'IN_CONTAINER'
+set -Eeuo pipefail
+
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+section() { printf '\n\033[1;34m=== %s ===\033[0m\n' "$*"; }
+
+PY="${PYTHON_BIN:-python3}"
+
+if [[ -d /usr/local/cuda ]]; then
+  export CUDA_HOME="${CUDA_HOME:-/usr/local/cuda}"
+  export PATH="${CUDA_HOME}/bin:${PATH}"
+  export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH:-}"
+fi
+
+export TRANSFORMERS_OFFLINE="${TRANSFORMERS_OFFLINE:-1}"
+export HF_HUB_OFFLINE="${HF_HUB_OFFLINE:-1}"
+export HF_HOME="${HF_HOME:-/tmp/hf-home}"
+mkdir -p "${HF_HOME}"
+
+section "1. Python 基础检查"
+command -v "${PY}" >/dev/null 2>&1 || fail "容器内未找到 Python 命令: ${PY}"
+"${PY}" --version
+ok "Python 可用"
+
+section "2. Python 包导入检查"
+"${PY}" - <<'PY'
+import importlib
+import platform
+import sys
+
+packages = ["torch", "transformers", "tokenizers"]
+print("python_executable:", sys.executable)
+print("platform:", platform.platform())
+
+for name in packages:
+    mod = importlib.import_module(name)
+    version = getattr(mod, "__version__", "unknown")
+    print(f"{name}: {version}")
+PY
+ok "torch / transformers / tokenizers 导入正常"
+
+section "3. CUDA / GPU 检查"
+if [[ "${REQUIRE_CUDA:-1}" == "1" ]]; then
+  command -v nvidia-smi >/dev/null 2>&1 || fail "nvidia-smi 不可用；请检查宿主机 NVIDIA 驱动、nvidia-container-toolkit、docker run --gpus 参数"
+  nvidia-smi --query-gpu=name,driver_version,memory.total --format=csv,noheader || fail "nvidia-smi 执行失败"
+
+  "${PY}" - <<'PY'
+import torch
+
+assert torch.cuda.is_available(), "torch.cuda.is_available() 为 False"
+print("torch_cuda_version:", torch.version.cuda)
+print("gpu_count:", torch.cuda.device_count())
+print("gpu_0_name:", torch.cuda.get_device_name(0))
+
+x = torch.randn(512, 512, device="cuda")
+y = x @ x
+torch.cuda.synchronize()
+print("gpu_matmul_shape:", tuple(y.shape))
+PY
+  ok "CUDA / GPU 可用，且 torch GPU 计算正常"
+else
+  warn "REQUIRE_CUDA=0，跳过强制 CUDA 检查"
+fi
+
+section "4. Transformers 离线功能检查：tokenizer + model forward + save/load + pipeline"
+"${PY}" - <<'PY'
+import os
+import tempfile
+import torch
+from transformers import (
+    AutoModel,
+    BertConfig,
+    BertForMaskedLM,
+    BertTokenizerFast,
+    pipeline,
+)
+
+require_cuda = os.environ.get("REQUIRE_CUDA", "1") == "1"
+device = torch.device("cuda:0" if require_cuda else "cpu")
+
+with tempfile.TemporaryDirectory() as tmp_dir:
+    vocab_path = os.path.join(tmp_dir, "vocab.txt")
+    with open(vocab_path, "w", encoding="utf-8") as f:
+        f.write("\n".join([
+            "[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]",
+            "hello", "world", "!", "transformers", "test", "good",
+        ]))
+
+    tokenizer = BertTokenizerFast(vocab_file=vocab_path, do_lower_case=True)
+    encoded = tokenizer("hello world !", return_tensors="pt", padding=True, truncation=True)
+
+    config = BertConfig(
+        vocab_size=tokenizer.vocab_size,
+        hidden_size=32,
+        num_hidden_layers=1,
+        num_attention_heads=4,
+        intermediate_size=64,
+        max_position_embeddings=64,
+    )
+
+    model = AutoModel.from_config(config).to(device).eval()
+    encoded_on_device = {k: v.to(device) for k, v in encoded.items()}
+    with torch.no_grad():
+        outputs = model(**encoded_on_device)
+
+    assert outputs.last_hidden_state.shape[0] == 1
+    assert outputs.last_hidden_state.shape[-1] == 32
+    print("forward_device:", str(device))
+    print("tokenizer_vocab_size:", tokenizer.vocab_size)
+    print("last_hidden_state_shape:", tuple(outputs.last_hidden_state.shape))
+
+    save_dir = os.path.join(tmp_dir, "tiny-bert")
+    model.save_pretrained(save_dir)
+    tokenizer.save_pretrained(save_dir)
+
+    loaded = AutoModel.from_pretrained(save_dir, local_files_only=True).to(device).eval()
+    with torch.no_grad():
+        loaded_outputs = loaded(**encoded_on_device)
+
+    assert loaded_outputs.last_hidden_state.shape == outputs.last_hidden_state.shape
+    print("local_save_load: ok")
+
+    mlm = BertForMaskedLM(config).to(device).eval()
+    fill_mask = pipeline(
+        "fill-mask",
+        model=mlm,
+        tokenizer=tokenizer,
+        device=0 if device.type == "cuda" else -1,
+        top_k=1,
+    )
+
+    result = fill_mask("hello [MASK] !")
+    assert isinstance(result, list) and len(result) == 1
+    assert "token_str" in result[0]
+    print("pipeline_fill_mask_token:", result[0]["token_str"])
+PY
+ok "Transformers 离线核心功能正常"
+
+section "5. 可选组件提示"
+if command -v nvcc >/dev/null 2>&1; then
+  nvcc --version | sed -n '1,4p'
+else
+  warn "未检测到 nvcc：运行时镜像通常不需要 nvcc，只有编译 CUDA 扩展时才需要"
+fi
+
+section "测试结果"
+ok "所有检查通过"
+IN_CONTAINER
+
+ok "宿主机侧 docker run 验证完成"
\ No newline at end of file
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