model : Qwen3 Next (#16095)

* Qwen3 Next - cleaned up version * Whitespaces and stuff * Correct minor errors * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Misc. fixes. * Clean up code, add missing hybrid qualifier * Did someone transpose the SOLVE_TRI result matrix? Perhaps... * Whitespace * Proper tensors for cb calls * Use llama-graph.h vertical alignment * BROKEN: chunking * Set new tensors as inputs. * Proper chunk logic * It's the circle of life... * More shenanigans for n_seq > 1 * Nail in the coffin? * Fix Windows build * Eh, one fails on Windows, the other fails on Mac... just use general capture. * quant : cleanup * model : cleanup * qwen3 : cleanup * cont : cleanup * cont : cleanup * ggml : revert change * qwen3 : cleanup * cont : cleanup * Readd cmath * qwen3 : fix typo * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Usual suspects * fix my bad suggestion --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-11-28 12:02:56 +01:00
parent 73955f7d2a
commit ff55414c42
16 changed files with 1345 additions and 19 deletions
@@ -4183,6 +4183,36 @@ class Qwen3MoeModel(Qwen2MoeModel):
        super().set_vocab()


+@ModelBase.register("Qwen3NextForCausalLM")
+class Qwen3NextModel(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_ssm_conv_kernel(self.hparams["linear_conv_kernel_dim"])
+        self.gguf_writer.add_ssm_state_size(self.hparams["linear_key_head_dim"])
+        self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
+        self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp"):
+            return [] # ignore MTP layers for now
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        elif name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+        elif "conv1d" in name:
+            data_torch = data_torch.squeeze()
+        elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
+            data_torch = data_torch + 1
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
@ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.RND1