Removing dumb idea

README.md

@@ -18,6 +18,8 @@ truecluster cluster --model Qwen/Qwen2.5-0.5B-Instruct --max-nodes 1 --quant fp1
 
 ## Run a node
 
+Nodes download/resolve the cluster model from HuggingFace themselves and load only the assigned layer range.
+
 ```bash
 truecluster node --cluster-host 127.0.0.1 --cluster-port 7001 --device auto
 ```

SPEC.md

@@ -2,7 +2,7 @@
 
 ## 1. Goal
 
-TrueCluster is a Python 3.10 prototype for distributed LLM inference across a small cluster of heterogeneous machines. It allows a coordinator/cluster process to load a HuggingFace safetensors model, split transformer layers across connected worker nodes, send each node only the weights it needs over a socket connection, and expose a simple OpenAI-compatible HTTP API for generation.
+TrueCluster is a Python 3.10 prototype for distributed LLM inference across a small cluster of heterogeneous machines. It allows a coordinator/cluster process to load a HuggingFace safetensors model config/tokenizer/coordinator-owned tensors, split transformer layers across connected worker nodes, tell each node which layer range to load from HuggingFace, and expose a simple OpenAI-compatible HTTP API for generation.
 
 The initial target is small Qwen2/Qwen2.5-style decoder-only models around 0.5B-1.5B parameters, with first-class support for mixed Nvidia CUDA and Apple Silicon Mac MPS nodes in the same cluster. Qwen3.5 hybrid linear-attention models are a later target and are not part of the first fp16 prototype.
 
@@ -12,8 +12,8 @@ Primary prototype goals:
 - Easy CLI for running a cluster or node.
 - Coordinator loads one model at a time.
 - Nodes connect to the coordinator by host/port.
-- Coordinator sends assigned model shards to nodes over sockets.
-- Nodes do not need local model files.
+- Coordinator sends layer assignments to nodes over sockets.
+- Nodes download/resolve the full HuggingFace model themselves, then load only their assigned layers.
 - Support HuggingFace safetensors models.
 - Initial architecture target: Qwen2/Qwen2.5-style causal language models.
 - Support `fp16`, portable `int8`, and portable `int4` weight-only quantization modes.
@@ -31,7 +31,7 @@ The first prototype will intentionally avoid several advanced features:
 - No expert parallelism.
 - No dynamic model hot-swapping.
 - No high-performance CUDA-only quantization kernels as a requirement.
-- No dependency on every node having HuggingFace access.
+- No coordinator-to-node weight transfer. Nodes are expected to have HuggingFace/model access.
 - No web UI.
 - No streaming responses in the first milestone.
 - No fault-tolerant recovery during an active generation.
@@ -49,7 +49,7 @@ The coordinator owns:
 - Tokenizer.
 - Sampling logic.
 - HuggingFace model metadata/config loading.
-- Model weight loading from safetensors.
+- Coordinator-owned tensor loading from safetensors.
 - Model split planning.
 - Embedding layer.
 - Final normalization.
@@ -81,7 +81,7 @@ HTTP request
   -> repeat until completion
 ```
 
-Weights are transferred once during assignment. During generation, only hidden states and small metadata are passed between coordinator and nodes.
+Weights are not transferred over the cluster socket. During assignment, the coordinator sends model id, config, and layer range. Each node downloads/resolves the model from HuggingFace or a matching local path and loads only its assigned layer tensors. During generation, only hidden states and small metadata are passed between coordinator and nodes.
 
 ## 4. Execution Model
 
@@ -173,12 +173,12 @@ Behavior:
 
 1. Resolve/download model.
 2. Load config and tokenizer.
-3. Load safetensors into coordinator RAM or memory-mapped index.
-4. Build model tensor index.
-5. Start node socket server.
-6. Start HTTP API.
-7. Wait for enough nodes.
-8. Assign layer ranges and transmit weights.
+3. Build safetensors metadata index and load only coordinator-owned tensors.
+4. Start node socket server.
+5. Start HTTP API.
+6. Wait for enough nodes.
+7. Assign layer ranges.
+8. Nodes download/resolve the model and load assigned layers.
 9. Mark cluster ready.
 
 ### 5.2 Node Command
@@ -199,7 +199,8 @@ Options:
 --cluster-port INT           Coordinator node socket port.
 --device TEXT                auto, cuda, cuda:0, mps, or cpu. Default: auto
 --node-id TEXT               Optional stable node id.
---work-dir PATH              Temporary local directory for received weights/cache.
+--hf-cache-dir PATH          Optional HuggingFace cache directory for node downloads.
+--work-dir PATH              Reserved for future temporary/cache files.
 --max-memory-gb FLOAT        Optional memory capability override.
 --log-level TEXT             Default: info
 ```
@@ -210,8 +211,8 @@ Behavior:
 2. Connect to coordinator socket.
 3. Send `HELLO` capability message.
 4. Wait for assignment.
-5. Receive config and layer weights.
-6. Build local model shard.
+5. Receive config and assigned layer range.
+6. Download/resolve the model locally and build local model shard.
 7. Mark itself ready.
 8. Serve prefill/decode requests over the persistent connection.
 
@@ -350,8 +351,6 @@ Coordinator/node lifecycle:
 HELLO
 HELLO_ACK
 ASSIGNMENT
-WEIGHT_CHUNK
-WEIGHTS_COMPLETE
 LOAD_COMPLETE
 LOAD_FAILED
 PING
@@ -432,32 +431,9 @@ Sent by coordinator after planning.
 }
 ```
 
-### 7.5 Tensor Transfer
+### 7.5 Model Loading on Nodes
 
-Each tensor chunk message includes metadata:
-
-```json
-{
-  "type": "WEIGHT_CHUNK",
-  "payload": {
-    "tensor_name": "model.layers.0.self_attn.q_proj.weight",
-    "dtype": "float16",
-    "shape": [1024, 1024],
-    "chunk_index": 0,
-    "chunk_count": 4,
-    "offset": 0,
-    "data": "binary payload or external raw section"
-  }
-}
-```
-
-For very large tensors, the preferred format is:
-
-```text
-[frame length][msgpack metadata][raw bytes referenced by metadata]
-```
-
-The implementation should keep this hidden behind `protocol/tensors.py`.
+The coordinator does not transfer model weights. After `ASSIGNMENT`, each node resolves/downloads `model_id` itself using HuggingFace cache semantics and loads only tensors whose names match its assigned layer range.
 
 ### 7.6 Inference Messages
 
@@ -666,8 +642,7 @@ This is designed for correctness and portability, not peak speed.
 Preferred prototype behavior:
 
 - Coordinator loads original safetensors.
-- Coordinator quantizes tensors before sending to nodes if `int8` or `int4` is selected.
-- Nodes receive already-quantized tensors plus quantization metadata.
+- Nodes quantize their locally loaded assigned tensors if `int8` or `int4` is selected.
 - Coordinator also quantizes/loads its own embedding/lm_head as needed.
 
 ## 11. Generation Algorithm
@@ -907,7 +882,7 @@ Deliverables:
 Deliverables:
 
 - Socket protocol.
-- Coordinator sends all transformer layers to one local node.
+- Coordinator assigns all transformer layers to one local node.
 - Node loads layers and runs them.
 - Coordinator keeps embedding/final norm/lm head.
 - `/v1/completions` works.
@@ -938,7 +913,7 @@ Deliverables:
 - `fp16` baseline.
 - Portable `int8` linear.
 - Portable `int4` linear.
-- Quantized weight transfer.
+- Node-local quantized layer loading.
 - CLI `--quant` option.
 
 ### Phase 6: API Polish
@@ -957,8 +932,8 @@ A prototype is considered working when:
 
 1. A cluster can be started with a HuggingFace safetensors Qwen-style model.
 2. A node can connect to the cluster with no local model files.
-3. The cluster sends layer weights to the node over the socket.
-4. The node loads assigned layers on CUDA, MPS, or CPU.
+3. The cluster sends a layer assignment to the node over the socket.
+4. The node downloads/resolves the model and loads assigned layers on CUDA, MPS, or CPU.
 5. `/v1/models` returns the loaded model.
 6. `/v1/completions` generates text through the distributed pipeline.
 7. `/v1/chat/completions` works for simple chat prompts.
@@ -971,7 +946,7 @@ A prototype is considered working when:
 - Use pipeline parallelism, not tensor parallelism.
 - Use contiguous layer ranges only.
 - Keep tokenizer, embeddings, final norm, lm head, and sampler on the coordinator.
-- Send weights once at node assignment time.
+- Do not send weights over the node socket; nodes load weights locally from HuggingFace/cache.
 - Send hidden states during generation.
 - Store KV cache on worker nodes.
 - Implement portable quantization instead of relying on CUDA-only libraries.

truecluster/cli.py

@@ -32,7 +32,6 @@ def cluster_cmd(
     max_nodes: int = typer.Option(1, "--max-nodes", min=1, help="Maximum/requested worker shard count"),
     quant: str = typer.Option("fp16", "--quant", help="Weight mode; currently only fp16 is implemented"),
     target_node_memory_gb: Optional[float] = typer.Option(None, "--target-node-memory-gb", help="Optional planner memory hint"),
-    weight_chunk_mb: float = typer.Option(4.0, "--weight-chunk-mb", min=0.0625, help="Model weight transfer chunk size in MiB"),
     trust_remote_code: bool = typer.Option(False, "--trust-remote-code", help="Allow HF remote code for config/tokenizer"),
     hf_cache_dir: Optional[str] = typer.Option(None, "--hf-cache-dir", help="Optional HuggingFace cache directory"),
     log_level: str = typer.Option("info", "--log-level"),
@@ -48,7 +47,6 @@ def cluster_cmd(
             max_nodes=max_nodes,
             quant=quant,
             target_node_memory_gb=target_node_memory_gb,
-            weight_chunk_mb=weight_chunk_mb,
             trust_remote_code=trust_remote_code,
             hf_cache_dir=hf_cache_dir,
         )
@@ -64,19 +62,17 @@ async def _run_cluster(
     max_nodes: int,
     quant: str,
     target_node_memory_gb: float | None,
-    weight_chunk_mb: float,
     trust_remote_code: bool,
     hf_cache_dir: str | None,
 ) -> None:
     log = logging.getLogger("truecluster.cluster")
-    log.info("loading model into coordinator RAM: %s", model)
+    log.info("loading coordinator model metadata/tokenizer/head tensors: %s", model)
     store = ModelStore.load(model, quant=quant, trust_remote_code=trust_remote_code, hf_cache_dir=hf_cache_dir)
     log.info("model loaded: %s layers, %s tensors", store.num_layers, len(store.tensors))
     runtime = ClusterRuntime(
         store,
         max_nodes=max_nodes,
         target_node_memory_gb=target_node_memory_gb,
-        weight_chunk_mb=weight_chunk_mb,
     )
     node_server = await start_node_server(node_host, node_port, runtime)
     api = create_app(runtime)
@@ -95,6 +91,7 @@ def node_cmd(
     node_id: Optional[str] = typer.Option(None, "--node-id", help="Optional stable node id"),
     work_dir: Optional[str] = typer.Option(None, "--work-dir", help="Reserved for future temporary weight storage"),
     max_memory_gb: Optional[float] = typer.Option(None, "--max-memory-gb", help="Optional capability override"),
+    hf_cache_dir: Optional[str] = typer.Option(None, "--hf-cache-dir", help="Optional HuggingFace cache directory for node downloads"),
     log_level: str = typer.Option("info", "--log-level"),
 ) -> None:
     _setup_logging(log_level)
@@ -107,6 +104,7 @@ def node_cmd(
             device=device,
             node_id=node_id,
             max_memory_gb=max_memory_gb,
+            hf_cache_dir=hf_cache_dir,
         )
     )
 

truecluster/cluster/model_store.py

@@ -1,12 +1,13 @@
 from __future__ import annotations
 
 import json
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 
 import torch
 from huggingface_hub import snapshot_download
-from safetensors.torch import load_file
+from safetensors import safe_open
 from transformers import AutoConfig, AutoTokenizer
 
 from truecluster.model.qwen import CoordinatorHead, QwenConfig
@@ -16,11 +17,39 @@ class ModelStoreError(RuntimeError):
     pass
 
 
+_DTYPE_BYTES = {
+    "BOOL": 1,
+    "U8": 1,
+    "I8": 1,
+    "I16": 2,
+    "U16": 2,
+    "F16": 2,
+    "BF16": 2,
+    "I32": 4,
+    "U32": 4,
+    "F32": 4,
+    "F64": 8,
+    "I64": 8,
+    "U64": 8,
+}
+
+
+@dataclass(frozen=True)
+class TensorInfo:
+    name: str
+    file: Path
+    shape: list[int]
+    dtype: str
+    nbytes: int
+
+
 class ModelStore:
     """Coordinator-side model owner.
 
-    The coordinator resolves the HF/local model, loads safetensors into RAM, and
-    keeps that RAM copy available for fast assignment transfer to worker nodes.
+    The coordinator resolves/downloads the model, loads tokenizer/config, and
+    only loads the small coordinator-owned tensors into RAM: embeddings, final
+    norm, and lm_head if present. Worker nodes download the same HuggingFace
+    model themselves and load only their assigned layer range.
     """
 
     def __init__(
@@ -30,6 +59,7 @@ class ModelStore:
         config_dict: dict[str, Any],
         tokenizer: Any,
         tensors: dict[str, torch.Tensor],
+        tensor_index: dict[str, TensorInfo],
         quant: str = "fp16",
     ):
         self.model_id = model_id
@@ -37,6 +67,7 @@ class ModelStore:
         self.config_dict = config_dict
         self.tokenizer = tokenizer
         self.tensors = tensors
+        self.tensor_index = tensor_index
         self.quant = quant
         self.qwen_config = QwenConfig.from_dict(config_dict)
         self.head = CoordinatorHead(config_dict, tensors, dtype=torch.float32)
@@ -52,12 +83,41 @@ class ModelStore:
         if quant != "fp16":
             raise ModelStoreError("only fp16 is implemented currently; int8/int4 are planned")
 
+        model_path, model_id = resolve_model_path(model, hf_cache_dir=hf_cache_dir)
+        config = AutoConfig.from_pretrained(str(model_path), trust_remote_code=trust_remote_code)
+        config_dict = _normalize_supported_config(config.to_dict())
+        tokenizer = AutoTokenizer.from_pretrained(str(model_path), trust_remote_code=trust_remote_code)
+        tensor_index = build_tensor_index(model_path)
+        _validate_qwen_tensor_names(config_dict, set(tensor_index))
+
+        head_names = ["model.embed_tokens.weight", "model.norm.weight"]
+        if "lm_head.weight" in tensor_index:
+            head_names.append("lm_head.weight")
+        tensors = load_selected_tensors(model_path, head_names)
+        _convert_float_tensors_to_fp16(tensors)
+        return cls(model_id, model_path, config_dict, tokenizer, tensors, tensor_index, quant=quant)
+
+    @property
+    def num_layers(self) -> int:
+        return self.qwen_config.num_hidden_layers
+
+    def tensors_for_layers(self, layer_start: int, layer_end_exclusive: int) -> dict[str, torch.Tensor]:
+        names = layer_tensor_names_from_index(self.tensor_index, layer_start, layer_end_exclusive)
+        tensors = load_selected_tensors(self.model_path, names)
+        _convert_float_tensors_to_fp16(tensors)
+        return tensors
+
+    def layer_bytes(self, layer_idx: int) -> int:
+        prefix = f"model.layers.{layer_idx}."
+        return sum(info.nbytes for name, info in self.tensor_index.items() if name.startswith(prefix))
+
+
+def resolve_model_path(model: str, hf_cache_dir: str | None = None) -> tuple[Path, str]:
     path = Path(model).expanduser()
     if path.exists():
-            model_path = path.resolve()
-            model_id = model
-        else:
-            model_path = Path(
+        return path.resolve(), model
+    return (
+        Path(
             snapshot_download(
                 repo_id=model,
                 cache_dir=hf_cache_dir,
@@ -70,41 +130,13 @@ class ModelStore:
                     "merges.txt",
                 ],
             )
+        ),
+        model,
     )
-            model_id = model
-
-        config = AutoConfig.from_pretrained(str(model_path), trust_remote_code=trust_remote_code)
-        config_dict = _normalize_supported_config(config.to_dict())
-        tokenizer = AutoTokenizer.from_pretrained(str(model_path), trust_remote_code=trust_remote_code)
-        tensors = _load_safetensors_into_ram(model_path)
-        _convert_float_tensors_to_fp16(tensors)
-        _validate_qwen_tensors(config_dict, tensors)
-        return cls(model_id, model_path, config_dict, tokenizer, tensors, quant=quant)
-
-    @property
-    def num_layers(self) -> int:
-        return self.qwen_config.num_hidden_layers
-
-    def tensors_for_layers(self, layer_start: int, layer_end_exclusive: int) -> dict[str, torch.Tensor]:
-        out: dict[str, torch.Tensor] = {}
-        prefixes = [f"model.layers.{i}." for i in range(layer_start, layer_end_exclusive)]
-        for name, tensor in self.tensors.items():
-            if any(name.startswith(prefix) for prefix in prefixes):
-                out[name] = tensor
-        return out
-
-    def layer_bytes(self, layer_idx: int) -> int:
-        prefix = f"model.layers.{layer_idx}."
-        return sum(t.numel() * t.element_size() for name, t in self.tensors.items() if name.startswith(prefix))
 
 
 def _normalize_supported_config(config_dict: dict[str, Any]) -> dict[str, Any]:
-    """Return the text decoder config for architectures this prototype supports.
-
-    The current runtime implements Qwen2/Qwen2.5-style full self-attention
-    decoder blocks. It intentionally does not implement Qwen3.5's hybrid
-    GatedDeltaNet/linear-attention blocks yet.
-    """
+    """Return the text decoder config for architectures this prototype supports."""
 
     model_type = config_dict.get("model_type")
     if model_type == "qwen3_5" or "text_config" in config_dict:
@@ -127,21 +159,67 @@ def _normalize_supported_config(config_dict: dict[str, Any]) -> dict[str, Any]:
     return config_dict
 
 
-def _load_safetensors_into_ram(model_path: Path) -> dict[str, torch.Tensor]:
+def build_tensor_index(model_path: Path) -> dict[str, TensorInfo]:
     files = sorted(model_path.glob("*.safetensors"))
     if not files:
         raise ModelStoreError(f"no safetensors files found in {model_path}")
 
-    tensors: dict[str, torch.Tensor] = {}
+    index: dict[str, TensorInfo] = {}
     for file in files:
-        part = load_file(str(file), device="cpu")
-        overlap = set(tensors).intersection(part)
-        if overlap:
-            raise ModelStoreError(f"duplicate tensor names in safetensors: {sorted(overlap)[:5]}")
-        tensors.update(part)
+        with safe_open(str(file), framework="pt", device="cpu") as f:
+            for name in f.keys():
+                if name in index:
+                    raise ModelStoreError(f"duplicate tensor name in safetensors: {name}")
+                view = f.get_slice(name)
+                shape = list(view.get_shape())
+                dtype = str(view.get_dtype())
+                elem_size = _DTYPE_BYTES.get(dtype)
+                if elem_size is None:
+                    raise ModelStoreError(f"unsupported safetensors dtype {dtype} for {name}")
+                nbytes = elem_size
+                for dim in shape:
+                    nbytes *= int(dim)
+                index[name] = TensorInfo(name=name, file=file, shape=shape, dtype=dtype, nbytes=nbytes)
+    return index
+
+
+def load_selected_tensors(model_path: Path, names: list[str]) -> dict[str, torch.Tensor]:
+    wanted = set(names)
+    tensors: dict[str, torch.Tensor] = {}
+    for file in sorted(model_path.glob("*.safetensors")):
+        with safe_open(str(file), framework="pt", device="cpu") as f:
+            available = wanted.intersection(f.keys())
+            for name in available:
+                tensors[name] = f.get_tensor(name).contiguous()
+    missing = wanted.difference(tensors)
+    if missing:
+        raise ModelStoreError(f"missing tensors: {sorted(missing)[:20]}")
     return tensors
 
 
+def load_layer_tensors_for_node(
+    model: str,
+    layer_start: int,
+    layer_end_exclusive: int,
+    hf_cache_dir: str | None = None,
+) -> dict[str, torch.Tensor]:
+    """Download/resolve the whole model and load only the assigned layer tensors."""
+
+    model_path, _ = resolve_model_path(model, hf_cache_dir=hf_cache_dir)
+    tensor_index = build_tensor_index(model_path)
+    names = layer_tensor_names_from_index(tensor_index, layer_start, layer_end_exclusive)
+    tensors = load_selected_tensors(model_path, names)
+    _convert_float_tensors_to_fp16(tensors)
+    return tensors
+
+
+def layer_tensor_names_from_index(
+    tensor_index: dict[str, TensorInfo], layer_start: int, layer_end_exclusive: int
+) -> list[str]:
+    prefixes = [f"model.layers.{i}." for i in range(layer_start, layer_end_exclusive)]
+    return [name for name in tensor_index if any(name.startswith(prefix) for prefix in prefixes)]
+
+
 def _convert_float_tensors_to_fp16(tensors: dict[str, torch.Tensor]) -> None:
     for name, tensor in list(tensors.items()):
         if tensor.is_floating_point() and tensor.dtype != torch.float16:
@@ -150,7 +228,7 @@ def _convert_float_tensors_to_fp16(tensors: dict[str, torch.Tensor]) -> None:
             tensors[name] = tensor.contiguous()
 
 
-def _validate_qwen_tensors(config_dict: dict[str, Any], tensors: dict[str, torch.Tensor]) -> None:
+def _validate_qwen_tensor_names(config_dict: dict[str, Any], tensor_names: set[str]) -> None:
     cfg = QwenConfig.from_dict(config_dict)
     required = ["model.embed_tokens.weight", "model.norm.weight"]
     for i in range(cfg.num_hidden_layers):
@@ -168,9 +246,12 @@ def _validate_qwen_tensors(config_dict: dict[str, Any], tensors: dict[str, torch
                 f"{p}.mlp.down_proj.weight",
             ]
         )
-    missing = [name for name in required if name not in tensors]
+    missing = [name for name in required if name not in tensor_names]
     if missing:
-        raise ModelStoreError("model does not look like supported Qwen-style safetensors; missing: " + ", ".join(missing[:20]))
+        raise ModelStoreError(
+            "model does not look like supported Qwen2/Qwen2.5-style safetensors; missing: "
+            + ", ".join(missing[:20])
+        )
 
 
 def read_config_json(model_path: Path) -> dict[str, Any]:

truecluster/cluster/server.py

@@ -16,7 +16,6 @@ from truecluster.protocol.framing import make_message, read_message, write_messa
 from truecluster.protocol.tensors import deserialize_tensor, serialize_tensor
 
 log = logging.getLogger(__name__)
-DEFAULT_WEIGHT_CHUNK_BYTES = 4 * 1024 * 1024
 
 
 @dataclass
@@ -57,7 +56,6 @@ class ClusterRuntime:
         model_store: ModelStore,
         max_nodes: int,
         target_node_memory_gb: float | None = None,
-        weight_chunk_mb: float = 4.0,
     ):
         self.model_store = model_store
         self.max_nodes = max_nodes
@@ -66,7 +64,6 @@ class ClusterRuntime:
         self.assignments = plan_even_layers(model_store.num_layers, max_nodes, target_bytes, layer_bytes)
         self.required_nodes = len(self.assignments)
         self.nodes: list[NodeHandle] = []
-        self.weight_chunk_bytes = max(64 * 1024, int(weight_chunk_mb * 1024 * 1024))
         self._assigning = False
         self.generation_lock = asyncio.Lock()
         log.info("planned %s assignment(s): %s", self.required_nodes, self.assignments)
@@ -106,14 +103,15 @@ class ClusterRuntime:
             self._assigning = False
 
     async def _assign_node(self, node: NodeHandle, assignment: LayerAssignment) -> None:
-        tensors = self.model_store.tensors_for_layers(assignment.layer_start, assignment.layer_end_exclusive)
-        total_bytes = sum(t.numel() * t.element_size() for t in tensors.values())
+        total_bytes = sum(
+            self.model_store.layer_bytes(i)
+            for i in range(assignment.layer_start, assignment.layer_end_exclusive)
+        )
         log.info(
-            "assigning node %s layers [%s,%s), tensors=%s, bytes=%.2f MB",
+            "assigning node %s layers [%s,%s), estimated bytes=%.2f MB; node will download/load model locally",
             node.node_id,
             assignment.layer_start,
             assignment.layer_end_exclusive,
-            len(tensors),
             total_bytes / 1024 / 1024,
         )
         async with node.lock:
@@ -127,13 +125,10 @@ class ClusterRuntime:
                     "layer_start": assignment.layer_start,
                     "layer_end_exclusive": assignment.layer_end_exclusive,
                     "config": self.model_store.config_dict,
-                    "tensor_count": len(tensors),
                     "total_weight_bytes": total_bytes,
+                    "weight_source": "huggingface_or_local_path",
                 },
             )
-            for name, tensor in tensors.items():
-                await self._send_weight_tensor(node, name, tensor)
-            await node.send(M.WEIGHTS_COMPLETE, {})
             reply = await node.recv()
         if reply.get("type") == M.LOAD_COMPLETE:
             node.ready = True
@@ -141,27 +136,6 @@ class ClusterRuntime:
             return
         raise RuntimeError(f"node {node.node_id} failed to load: {reply}")
 
-    async def _send_weight_tensor(self, node: NodeHandle, name: str, tensor: torch.Tensor) -> None:
-        serialized = serialize_tensor(tensor)
-        data = serialized["data"]
-        total_bytes = len(data)
-        chunk_count = max(1, (total_bytes + self.weight_chunk_bytes - 1) // self.weight_chunk_bytes)
-        for chunk_index in range(chunk_count):
-            start = chunk_index * self.weight_chunk_bytes
-            end = min(start + self.weight_chunk_bytes, total_bytes)
-            await node.send(
-                M.WEIGHT_CHUNK,
-                {
-                    "name": name,
-                    "dtype": serialized["dtype"],
-                    "shape": serialized["shape"],
-                    "total_bytes": total_bytes,
-                    "chunk_index": chunk_index,
-                    "chunk_count": chunk_count,
-                    "data": data[start:end],
-                },
-            )
-
     async def clear_caches(self) -> None:
         for node in self.ready_nodes[: self.required_nodes]:
             async with node.lock:

truecluster/node/client.py

@@ -18,9 +18,10 @@ async def run_node(
     device: str = "auto",
     node_id: str | None = None,
     max_memory_gb: float | None = None,
+    hf_cache_dir: str | None = None,
 ) -> None:
     selected = select_device(device)
-    runtime = NodeRuntime(selected)
+    runtime = NodeRuntime(selected, hf_cache_dir=hf_cache_dir)
     log.info("connecting to cluster %s:%s using device %s", cluster_host, cluster_port, selected)
     reader, writer = await asyncio.open_connection(cluster_host, cluster_port)
     await write_message(writer, make_message(M.HELLO, capabilities(selected, node_id=node_id, max_memory_gb=max_memory_gb)))
@@ -38,23 +39,18 @@ async def run_node(
                 elif msg_type == M.ASSIGNMENT:
                     runtime.set_assignment(payload)
                     log.info(
-                        "received assignment: layers [%s,%s), tensors=%s, bytes=%.2f MB",
+                        "received assignment: model=%s layers [%s,%s), estimated bytes=%.2f MB",
+                        payload.get("model_id"),
                         payload.get("layer_start"),
                         payload.get("layer_end_exclusive"),
-                        payload.get("tensor_count"),
                         int(payload.get("total_weight_bytes", 0)) / 1024 / 1024,
                     )
-                elif msg_type == M.WEIGHT_TENSOR:
-                    runtime.add_tensor(payload["name"], deserialize_tensor(payload["tensor"], device="cpu"))
-                elif msg_type == M.WEIGHT_CHUNK:
-                    completed = runtime.add_tensor_chunk(payload)
-                    if completed and len(runtime.tensors) % 25 == 0:
-                        log.info("received %s tensors", len(runtime.tensors))
-                elif msg_type == M.WEIGHTS_COMPLETE:
-                    log.info("all weights received; loading shard")
+                    log.info("downloading/resolving model locally and loading assigned layers")
                     runtime.load()
                     await write_message(writer, make_message(M.LOAD_COMPLETE, {"device": selected}))
                     log.info("shard loaded and ready")
+                elif msg_type in (M.WEIGHT_TENSOR, M.WEIGHT_CHUNK, M.WEIGHTS_COMPLETE):
+                    log.warning("cluster tried to send weights, but this protocol now expects local node downloads")
                 elif msg_type == M.CLEAR_CACHE:
                     runtime.clear_cache()
                 elif msg_type in (M.RUN_PREFILL, M.RUN_DECODE):

truecluster/node/runtime.py

@@ -4,61 +4,33 @@ from typing import Any
 
 import torch
 
-from truecluster.protocol.tensors import deserialize_tensor
-
+from truecluster.cluster.model_store import load_layer_tensors_for_node
 from truecluster.model.qwen import QwenLayerShard
 
 
 class NodeRuntime:
-    def __init__(self, device: str):
+    def __init__(self, device: str, hf_cache_dir: str | None = None):
         self.device = device
+        self.hf_cache_dir = hf_cache_dir
         self.assignment: dict[str, Any] | None = None
         self.tensors: dict[str, torch.Tensor] = {}
-        self._tensor_chunks: dict[str, dict[str, Any]] = {}
         self.shard: QwenLayerShard | None = None
 
     def set_assignment(self, payload: dict[str, Any]) -> None:
         self.assignment = payload
         self.tensors = {}
-        self._tensor_chunks = {}
         self.shard = None
 
-    def add_tensor(self, name: str, tensor: torch.Tensor) -> None:
-        self.tensors[name] = tensor
-
-    def add_tensor_chunk(self, payload: dict[str, Any]) -> bool:
-        """Add one incoming weight chunk.
-
-        Returns True when the full tensor has been reassembled.
-        """
-
-        name = payload["name"]
-        entry = self._tensor_chunks.setdefault(
-            name,
-            {
-                "dtype": payload["dtype"],
-                "shape": payload["shape"],
-                "total_bytes": int(payload["total_bytes"]),
-                "chunk_count": int(payload["chunk_count"]),
-                "chunks": {},
-            },
-        )
-        entry["chunks"][int(payload["chunk_index"])] = payload["data"]
-        if len(entry["chunks"]) != entry["chunk_count"]:
-            return False
-        data = b"".join(entry["chunks"][i] for i in range(entry["chunk_count"]))
-        if len(data) != entry["total_bytes"]:
-            raise RuntimeError(f"reassembled tensor {name} has {len(data)} bytes, expected {entry['total_bytes']}")
-        self.tensors[name] = deserialize_tensor(
-            {"dtype": entry["dtype"], "shape": entry["shape"], "data": data},
-            device="cpu",
-        )
-        del self._tensor_chunks[name]
-        return True
-
     def load(self) -> None:
         if self.assignment is None:
             raise RuntimeError("no assignment received")
+        if not self.tensors:
+            self.tensors = load_layer_tensors_for_node(
+                self.assignment["model_id"],
+                int(self.assignment["layer_start"]),
+                int(self.assignment["layer_end_exclusive"]),
+                hf_cache_dir=self.hf_cache_dir,
+            )
         self.shard = QwenLayerShard(
             config_dict=self.assignment["config"],
             layer_start=int(self.assignment["layer_start"]),
@@ -67,7 +39,7 @@ class NodeRuntime:
             device=self.device,
             dtype=torch.float16 if self.device != "cpu" else torch.float32,
         )
-        # Release CPU transfer tensors after materializing the shard.
+        # Release CPU tensors after materializing the shard on the selected device.
         self.tensors = {}
 
     def clear_cache(self) -> None: