Document transformers>=5.12 requirement and Qwen3.5/3.6-MoE fast-path notes

Bump the node package's transformers floor to 5.12 (older versions lack
composite Qwen3_5MoeConfig handling and fail with missing vocab_size), and
explain in QUICKSTART/INSTALL_WINDOWS that the flash-linear-attention /
causal-conv1d fast-path warning is a harmless CPU fallback.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

QUICKSTART.md

@@ -24,12 +24,28 @@ python3 -m venv .venv
 .venv/bin/pip install torch --index-url https://download.pytorch.org/whl/cpu
 
 # HuggingFace model libraries
-.venv/bin/pip install transformers accelerate
+.venv/bin/pip install "transformers>=5.12" accelerate
 ```
 
 > **NVIDIA GPU (CUDA):** replace the torch line with `pip install torch` (default index).  
 > **AMD GPU (ROCm):** `pip install torch --index-url https://download.pytorch.org/whl/rocm6.2`
 
+### Version and library notes for Qwen3.5/3.6-MoE models
+
+- **transformers ≥ 5.12 is required** for Qwen3.5/3.6-MoE (e.g. `Qwen3.6-35B-A3B`).
+  Older versions fail at load time with
+  `'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`. Check with
+  `python -c "import transformers; print(transformers.__version__)"` and upgrade
+  with `pip install -U transformers` in the environment that runs `meshnet-node`
+  (conda/miniforge users: upgrade inside that env, not a layered `.venv`).
+- The startup warning
+  `The fast path is not available because one of the required library is not installed`
+  is **harmless** — transformers falls back to a pure-torch implementation of the
+  linear-attention layers. The fast-path packages (`flash-linear-attention`,
+  `causal-conv1d`) are CUDA-only kernels: install them for GPU speed if you want,
+  skip them entirely on CPU nodes.
+- `pip install nvidia-ml-py` silences the pynvml deprecation warning on NVIDIA hosts.
+
 ## Bootstrap a tracker on a new machine
 
 Use this when provisioning a fresh LAN/public tracker host. The tracker itself is
@@ -112,7 +128,7 @@ python3 -m venv .venv
 .venv/bin/python -m pip install --upgrade pip setuptools wheel
 .venv/bin/pip install -e packages/tracker -e packages/node -e packages/p2p -e packages/gateway -e packages/relay
 .venv/bin/pip install torch --index-url https://download.pytorch.org/whl/cpu
-.venv/bin/pip install transformers accelerate
+.venv/bin/pip install "transformers>=5.12" accelerate
 .venv/bin/meshnet-node --help
 ```
 
@@ -145,9 +161,15 @@ Install project packages into the active conda/miniforge env:
 cd D:\DEV\workspace\REPOS\git.d-popov.com\neuron-tai
 
 pip install -e packages\tracker -e packages\node -e packages\p2p -e packages\gateway -e packages\relay
-pip install transformers accelerate safetensors   # torch is already present
+pip install "transformers>=5.12" accelerate safetensors   # torch is already present
 ```
 
+> Conda/miniforge envs often carry an older `transformers` pinned by other tools
+> (aider, etc.). Qwen3.5/3.6-MoE models need **transformers ≥ 5.12** — verify with
+> `python -c "import transformers; print(transformers.__version__)"`. The pip
+> resolver may print dependency-conflict warnings for those other tools; they don't
+> affect `meshnet-node`.
+
 Verify torch is importable and CUDA is live **before** starting the node:
 
 ```powershell
@@ -213,7 +235,7 @@ python -m venv .venv
 
 # CPU-only PyTorch. For NVIDIA CUDA, use `pip install torch` instead.
 .\.venv\Scripts\pip.exe install torch --index-url https://download.pytorch.org/whl/cpu
-.\.venv\Scripts\pip.exe install transformers accelerate
+.\.venv\Scripts\pip.exe install "transformers>=5.12" accelerate
 
 .\.venv\Scripts\meshnet-node.exe --help
 ```

docs/INSTALL_WINDOWS.md

@@ -103,8 +103,16 @@ Verify the install:
 
 ```bash
 meshnet-node --help
+python -c "import transformers; print(transformers.__version__)"
 ```
 
+`transformers` must be **≥ 5.12** for Qwen3.5/3.6-MoE models (older versions fail
+with `'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`). If you install
+into an existing conda/miniforge env instead of a fresh venv, run
+`pip install -U transformers` there. The startup warning about
+`flash-linear-attention` / `causal-conv1d` ("fast path is not available") is
+harmless on CPU — those are optional CUDA-only kernels.
+
 ---
 
 ## Step 6 — Pre-download the model shard

packages/node/pyproject.toml

@@ -16,7 +16,7 @@ dependencies = [
     "rich>=13",
     "safetensors>=0.4",
     "torch>=2.1",
-    "transformers>=4.39",
+    "transformers>=5.12",
     "websockets>=13",
     "zstandard>=0.22",
     "kernels>=0.11.1,<0.16",