Adds a committed .gitattributes so Windows and Linux checkouts converge
on LF for all text files, overriding each developer's local core.autocrlf.
Renormalizes existing blobs (server.py, dashboard.html, etc.) that had
CRLF baked in, clearing the repo-wide phantom "modified" churn.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Qwen3.5/3.6-MoE checkpoints ship vision (model.visual.*) and multi-token-
prediction (mtp.*) weights; the partial shard loader assigned them into the
text-only Qwen3_5MoeForCausalLM and crashed with AttributeError 'mtp'.
Filter selected tensors against the built model's state_dict keys, matching
transformers' _keys_to_ignore_on_load_unexpected behavior.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
When _select_route picks two nodes with overlapping registrations (e.g.
A:0-22 and B:20-24), the tracker now injects start_layer per hop so B
executes only layers 23-24, not 20-24.
- model_backend: forward_bytes + _run_layers accept start_layer offset;
clamped to shard_start to prevent out-of-bounds indexing
- torch_server: _handle_binary_forward reads X-Meshnet-Start-Layer header;
_run_downstream_pipeline sends it per hop; route is now list[tuple[str,int]]
- server: proxy injects {endpoint, start_layer} objects in X-Meshnet-Route;
/v1/route response includes start_layer per node in the nodes list
- test: fake backends accept start_layer=None kwarg
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Single-node mode now uses HF model.generate() instead of one-shot
decode_tail(), giving correct multi-token output with KV cache.
model_backend.py:
- generate_text(messages, max_new_tokens, temperature, top_p) — full
autoregressive generation via model.generate() with chat template
- generate_text_streaming() — yields token strings via TextIteratorStreamer
- _encode_messages() — applies chat template (tokenize=False then tokenize),
falls back to joining user messages; avoids BatchEncoding issues
torch_server.py:
- _handle_chat_completions: fast path when backend is head+tail — calls
generate_text() or generate_text_streaming() directly instead of the
single-token encode_prompt+decode_tail pipeline
- _stream_openai_response: new SSE streaming handler for token iterators
- Parses max_tokens, temperature, top_p from request body
- Distributed path (partial shards) unchanged
Verified: streaming and non-streaming both work with Qwen2.5-0.5B-Instruct.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
model_backend.py was using Python-style exclusive end (layers[start:end])
while all callers (CLI, tests, QUICKSTART) use inclusive 0-based indexing.
Result: 24-layer model with shard_end=23 ran only 23 layers and never
set is_tail=True, so decode_tail() was never called and responses were empty.
- is_tail: == total_layers → >= total_layers - 1
- _run_layers: layers[start:end] → layers[start:end+1]
- Validation: > total_layers → >= total_layers (was also wrong)
Inference confirmed: Qwen2.5-0.5B-Instruct now returns real LLM output.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
