* model : fold Ling 3.0 VL into the BailingMoeV3 architecture
Assisted-by: Scout
* model : keep shared NORM rope list intact when gating bailingmoe3 on mrope sections
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Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
* chat: add dedicated Ling 3.0 (Bailing V3) parser
Ling 3.0 Flash templates pre-open the think block in the generation
prompt, so the model never emits an opening <think>, and a tool call can
arrive before any </think>. The generated autoparser terminated reasoning
only at the close tag, which classified such tool calls entirely as
reasoning_content: clients received content="" with no tool_calls and
agent loops died as reasoning-only turns.
Adds a specialized parser that terminates reasoning at the think close
tag or at a <tool_call> start, mirroring the hand-written Qwen3-Coder and
Kimi K3 parsers and the reference vLLM/SGLang Ling3 parser (which treats
<tool_call> as an implicit reasoning terminator). Detection is gated on
the <role>...</role> section markers, unique to this family among the
tagged-argument templates.
Adds the Ling 3.0 Flash chat template and tests covering the
unclosed-think tool call (full parse and streaming), healthy closed-think
paths, trailing prose, parallel calls, marker-like strings in argument
values, string-union and non-string argument types, and
reasoning_format=none.
Assisted-by: Kimi Code
* tests : move Ling 3.0 test
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Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
Co-authored-by: Alde Rojas <hello@alde.dev>
* Adding support for bailingmoe3
* Adds speculative decoding support
* Make BailingMoE3 safe gate metadata optional
* bailingmoe3: apply trained SwiGLU clamps
* common: fix Bailing V3 tool argument parsing
* llama-model-saver, instantiate float vector metadata writer
* bailingmoe3: support Q-LoRA (Ling-3.0-tiny)
Ling-3.0-flash sets q_lora_rank: None and projects Q directly, so the current
implementation loads a single ATTN_Q tensor. Ling-3.0-tiny sets q_lora_rank: 256
and routes Q through a LoRA bottleneck instead:
q_a_proj -> q_a_layernorm -> q_b_proj
Conversion therefore failed with:
ValueError: Can not map tensor 'model.layers.3.attention.q_a_layernorm.weight'
Add the missing path, mirroring the existing deepseek2 MLA implementation:
* constants.py - add ATTN_Q_A / ATTN_Q_B / ATTN_Q_A_NORM to BAILINGMOE3
* tensor_mapping.py - map model.layers.{bid}.attention.q_{a,b}_proj and
q_a_layernorm
* conversion - emit attention.q_lora_rank when the config has it
* bailingmoe3.cpp - read n_lora_q; create the Q-LoRA tensors and build Q
through the bottleneck when q_lora_rank > 0
Everything is gated on q_lora_rank > 0. Ling-3.0-flash's config has no
q_lora_rank, the converter only emits the key when present, hparams.n_lora_q
defaults to 0, and get_key(..., required=false) leaves the target untouched when
the key is absent - so flash keeps taking the existing direct-Q branch.
The LoRA path produces the same shape as the direct projection, so the
nope/rope split, RoPE application and wk_b absorption downstream are unchanged.
* small mtp change
* bailingmoe3: support separate MTP GGUF and Q-LoRA MTP
* gguf: remove duplicate add_kda_gate_lower_bound definition
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Co-authored-by: bloomer <bloomer@booper.brushtail.me>
Co-authored-by: Dyluhn <dylanranejohnston1@gmail.com>
Tobyandaetherbird
