* model: add Hy3 (hy_v3) architecture support
Adds Tencent Hunyuan 3 (HF architecture HYV3ForCausalLM, GGUF arch
hy_v3): a MoE decoder stack with per-head Q/K RMSNorm, a sigmoid
router with expert selection bias, an always-active ungated shared
expert, and leading dense block(s) (first_k_dense_replace).
The base implementation is ported from charlie12345's fork
(https://github.com/charlie12345/ROCmFPX, src/models/hyv3.cpp),
adapted to current mainline APIs (hparams.n_layer(), build_qkv,
build_moe_ffn with fused gate_up + scale tensors, output_s).
Note: blk.N.exp_probs_b is stored without a .bias suffix for
compatibility with existing hy_v3 GGUFs produced by that fork.
Co-Authored-By: charlie12345 <charlie12345@users.noreply.github.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
Assisted-by: Claude Fable 5
* chat: harden peg-native tool call parsing
accept an optional leading type: function field in
build_json_tools_flat_keys so openai style tool calls parse on
templates whose serialization opens on the name field.
return a clean error and log the unparsed fragment on a final peg
parse failure instead of throwing the raw parser position and input.
keep the raw arguments string in func_args_not_string when it is not
valid json instead of aborting the prompt render.
* chat: surface peg-native parse failures
a final peg parse failure threw the raw parser position and input. log
the unparsed fragment and raise a clearer error instead, so a model
output that does not match the expected format no longer fails silently
with an empty assistant turn.
minimal change, no behavior change on successful parses.
* chat: handle openai style tool calls in peg-native
* nits
* common: scope OpenAI wrapper grammar trigger via autoparser flag
* chat: gate type:function parsing leniency on the analysis flag
Thread accept_openai_wrapper from the generator to build_json_tools_flat_keys
so the leading "type": "function" field is accepted only when openai_wrapper_trigger is set.
* common : add common_chat_split_by_role
* cont : fix spans to reach end of message
* server: fix checkpoints creation
- extract message_spans from chat templates
- find the prompt token position before the latest user message
- split prompt batching at that position
- create a context checkpoint before the latest user input
- avoid periodic mid-prompt checkpoints when that position is known
- handle multimodal prompts when mapping text/template positions to server prompt tokens
- add --checkpoint-min-step to control minimum spacing between checkpoints
* cont : clean-up
* Support autoparser detection for message barriers
* server: fix message span delimiter and update docs
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
* common : delegate assistant continuation to template handler
* server : implement echo parameter to exclude assistant prefill in the response
* server : fix tests for prefill
* server : use existing llama template
* cont : clean up
