Add custom paths for qwen3 and 3.5 dense

src/prime_rl/trainer/model.py

@@ -225,6 +225,13 @@ def get_model(
             config.name, attn_implementation=config.attn, trust_remote_code=config.trust_remote_code
         ),
     )
+    if not is_vlm_training and getattr(model_config, "model_type", "") == "qwen3_5":
+        logger.info(f"Using text-only Qwen3.5 config path for {config.name}")
+        text_config = cast(PretrainedConfig, model_config.text_config)
+        text_config._attn_implementation = getattr(model_config, "_attn_implementation", config.attn)
+        text_config._name_or_path = getattr(model_config, "_name_or_path", config.name)
+        model_config = text_config
+
     model_config.use_cache = False
     is_vlm_arch = is_vlm_architecture(model_config)
 

src/prime_rl/trainer/models/__init__.py

@@ -7,6 +7,8 @@
 from transformers.models.auto.auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
 from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES
 from transformers.models.llama.configuration_llama import LlamaConfig
+from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
+from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig
 
 from prime_rl.trainer.models.afmoe import AfmoeConfig, AfmoeForCausalLM
 from prime_rl.trainer.models.base import PreTrainedModelPrimeRL
@@ -16,6 +18,8 @@
 from prime_rl.trainer.models.llama import LlamaForCausalLM
 from prime_rl.trainer.models.minimax_m2 import MiniMaxM2Config, MiniMaxM2ForCausalLM
 from prime_rl.trainer.models.nemotron_h import NemotronHConfig, NemotronHForCausalLM
+from prime_rl.trainer.models.qwen3 import Qwen3ForCausalLM
+from prime_rl.trainer.models.qwen3_5 import Qwen3_5ForCausalLM
 from prime_rl.trainer.models.qwen3_5_moe import Qwen3_5MoeConfig, Qwen3_5MoeForCausalLM
 from prime_rl.trainer.models.qwen3_moe import Qwen3MoeConfig, Qwen3MoeForCausalLM
 
@@ -35,6 +39,8 @@
 _CUSTOM_CAUSAL_LM_MAPPING.register(GlmMoeDsaConfig, GlmMoeDsaForCausalLM, exist_ok=True)
 _CUSTOM_CAUSAL_LM_MAPPING.register(MiniMaxM2Config, MiniMaxM2ForCausalLM, exist_ok=True)
 _CUSTOM_CAUSAL_LM_MAPPING.register(NemotronHConfig, NemotronHForCausalLM, exist_ok=True)
+_CUSTOM_CAUSAL_LM_MAPPING.register(Qwen3Config, Qwen3ForCausalLM, exist_ok=True)
+_CUSTOM_CAUSAL_LM_MAPPING.register(Qwen3_5TextConfig, Qwen3_5ForCausalLM, exist_ok=True)
 _CUSTOM_CAUSAL_LM_MAPPING.register(Qwen3MoeConfig, Qwen3MoeForCausalLM, exist_ok=True)
 _CUSTOM_CAUSAL_LM_MAPPING.register(Qwen3_5MoeConfig, Qwen3_5MoeForCausalLM, exist_ok=True)
 

src/prime_rl/trainer/models/layers/attn.py

@@ -325,9 +325,11 @@ def _ring_compute_attention(self, q, k, v, cu_seqlens, max_seqlen):
     FlashAttention._compute_attention = _ring_compute_attention
 
     from prime_rl.trainer.models.afmoe.modeling_afmoe import AfmoeFlashAttention
-
-    AfmoeFlashAttention._compute_attention = _ring_compute_attention
-
+    from prime_rl.trainer.models.qwen3.modeling_qwen3 import Qwen3FlashAttention
+    from prime_rl.trainer.models.qwen3_5.modeling_qwen3_5 import Qwen3_5FlashAttention
     from prime_rl.trainer.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeGatedFlashAttention
 
+    AfmoeFlashAttention._compute_attention = _ring_compute_attention
+    Qwen3FlashAttention._compute_attention = _ring_compute_attention
+    Qwen3_5FlashAttention._compute_attention = _ring_compute_attention
     Qwen3_5MoeGatedFlashAttention._compute_attention = _ring_compute_attention

src/prime_rl/trainer/models/qwen3/__init__.py

@@ -0,0 +1,14 @@
+from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
+
+from prime_rl.trainer.models.qwen3.modeling_qwen3 import (
+    Qwen3ForCausalLM,
+    Qwen3Model,
+    Qwen3PreTrainedModel,
+)
+
+__all__ = [
+    "Qwen3Config",
+    "Qwen3ForCausalLM",
+    "Qwen3Model",
+    "Qwen3PreTrainedModel",
+]