Qwen3 Dense

README.md

@@ -146,6 +146,7 @@ Every model is written from scratch to maximize performance and remove layers of
 | Qwen2.5 Math | 1.5B, 7B, 72B | Alibaba Group | [An, Yang et al. 2024](https://arxiv.org/abs/2409.12122)                                          |
 | QwQ | 32B | Alibaba Group | [Qwen Team 2025](https://qwenlm.github.io/blog/qwq-32b/)                                                                         |
 | QwQ-Preview | 32B | Alibaba Group | [Qwen Team 2024](https://qwenlm.github.io/blog/qwq-32b-preview/)                                                                         |
+| Qwen3 | 0.6B, 1.7B, 4B, 8B, 14B, 32B | Alibaba Group | [Qwen Team 2025](https://arxiv.org/abs/2505.09388/)                                                                         |
 | R1 Distill Llama | 8B, 70B | DeepSeek AI | [DeepSeek AI 2025](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf)                                                                                 |
 | SmolLM2 | 135M, 360M, 1.7B | Hugging Face | [Hugging Face 2024](https://github.com/huggingface/smollm)                                                               |
 | Salamandra | 2B, 7B | Barcelona Supercomputing Centre | [BSC-LTC 2024](https://github.com/BSC-LTC/salamandra)                                                                         |

litgpt/config.py

@@ -2460,6 +2460,147 @@ def norm_class(self) -> Type:
 
 configs.extend(qwq)
 
+qwen_3 = [
+    # https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/config.json
+    dict(
+        name="Qwen3-0.6B{}",
+        hf_config=dict(org="Qwen", name="Qwen3-0.6B{}"),
+        block_size=40960,
+        vocab_size=151643,
+        padded_vocab_size=151936,
+        n_layer=28,
+        n_head=16,
+        n_embd=1024,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=3072,
+        norm_eps=1e-6,
+        rope_base=1000000,
+        head_size=128,
+        norm_qk=True,
+    ),
+    # https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/config.json
+    dict(
+        name="Qwen3-1.7B{}",
+        hf_config=dict(org="Qwen", name="Qwen3-1.7B{}"),
+        block_size=40960,
+        vocab_size=151643,
+        padded_vocab_size=151936,
+        n_layer=28,
+        n_head=16,
+        n_embd=2048,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=6144,
+        norm_eps=1e-6,
+        rope_base=1000000,
+        norm_qk=True,
+    ),
+    # https://huggingface.co/Qwen/Qwen3-4B/blob/main/config.json
+    dict(
+        name="Qwen3-4B{}",
+        hf_config=dict(org="Qwen", name="Qwen3-4B{}"),
+        block_size=40960,
+        vocab_size=151643,
+        padded_vocab_size=151936,
+        n_layer=36,
+        n_head=32,
+        n_embd=2560,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=9728,
+        norm_eps=1e-6,
+        rope_base=1000000,
+        head_size=128,
+        norm_qk=True,
+    ),
+    # https://huggingface.co/Qwen/Qwen3-8B/blob/main/config.json
+    dict(
+        name="Qwen3-8B{}",
+        hf_config=dict(org="Qwen", name="Qwen3-8B{}"),
+        block_size=40960,
+        vocab_size=151643,
+        padded_vocab_size=151936,
+        n_layer=36,
+        n_head=32,
+        n_embd=4096,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=12288,
+        norm_eps=1e-6,
+        rope_base=1000000,
+        norm_qk=True,
+    ),
+    # https://huggingface.co/Qwen/Qwen3-14B/blob/main/config.json
+    dict(
+        name="Qwen3-14B{}",
+        hf_config=dict(org="Qwen", name="Qwen3-14B{}"),
+        block_size=40960,
+        vocab_size=151643,
+        padded_vocab_size=151936,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=17408,
+        norm_eps=1e-6,
+        rope_base=1000000,
+        norm_qk=True,
+    ),
+]
+for c in qwen_3:
+    for kind in ("", "-Base"):
+        copy = deepcopy(c)
+        copy["name"] = c["name"].format(kind)
+        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
+        configs.append(copy)
+qwen_3_32b = [
+    # https://huggingface.co/Qwen/Qwen3-32B/blob/main/config.json
+    dict(
+        name="Qwen3-32B",
+        hf_config=dict(org="Qwen", name="Qwen3-32B"),
+        block_size=40960,
+        vocab_size=151643,
+        padded_vocab_size=151936,
+        n_layer=64,
+        n_head=64,
+        n_embd=5120,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=25600,
+        norm_eps=1e-6,
+        rope_base=1000000,
+        head_size=128,
+        norm_qk=True,
+    ),
+]
+configs.extend(qwen_3_32b)
+
 
 #############
 # Salamandra

litgpt/prompts.py

@@ -345,7 +345,7 @@ def apply(self, prompt: str, *, sys_prompt: Optional[str] = None, **kwargs: str)
 
 
 class ChatML(PromptStyle):
-    def __init__(self, system_message: str):
+    def __init__(self, system_message: Optional[str] = None):
         self.system_message = system_message
 
     def apply(self, prompt: str, *, sys_prompt: Optional[str] = None, **kwargs: str) -> str:
@@ -372,6 +372,11 @@ def __init__(self):
         )
 
 
+class Qwen3(ChatML):
+    def __init__(self):
+        super().__init__()
+
+
 class SmolLM2(ChatML):
     def __init__(self):
         super().__init__("You are a helpful AI assistant named SmolLM, trained by Hugging Face")
@@ -411,6 +416,7 @@ def __init__(self):
     "qwen2.5": Qwen2_5,
     "qwen2.5-math": Qwen2_5_Math,
     "qwq": QwQ,
+    "qwen3": Qwen3,
     "smollm2": SmolLM2,
     "salamandra": Salamandra,
 }
@@ -463,6 +469,8 @@ def model_name_to_prompt_style(model_name: str) -> PromptStyle:
         return Qwen2_5()
     if re.search(r"QwQ-.*", model_name):
         return QwQ()
+    if re.search(r"Qwen3-.*", model_name):
+        return Qwen3()
     if re.search(r"SmolLM2.*-Instruct", model_name):
         return SmolLM2()
     if re.search(r"salamandra-.*-instruct", model_name):

litgpt/scripts/convert_hf_checkpoint.py

@@ -533,6 +533,75 @@ def copy_weights_qwen_2_5(
                 pbar.update(progress_per_file)
 
 
+def copy_weights_qwen_3(
+    config: Config,
+    qkv_weights: Dict[int, List[Optional[NotYetLoadedTensor]]],
+    state_dict: Dict[str, torch.Tensor],
+    hf_weights: Dict[str, Union[torch.Tensor, NotYetLoadedTensor]],
+    saver: Optional[incremental_save] = None,
+    dtype: Optional[torch.dtype] = None,
+    pbar: Optional[tqdm] = None,
+    progress_per_file: Optional[float] = None,
+    debug_mode: Optional[bool] = False,
+) -> None:
+    weight_map = {
+        "model.embed_tokens.weight": "transformer.wte.weight",
+        "model.layers.{}.input_layernorm.weight": "transformer.h.{}.norm_1.weight",
+        "model.layers.{}.self_attn.q_proj.weight": None,
+        "model.layers.{}.self_attn.k_proj.weight": None,
+        "model.layers.{}.self_attn.v_proj.weight": None,
+        "model.layers.{}.self_attn.q_norm.weight": "transformer.h.{}.attn.norm_q.weight",
+        "model.layers.{}.self_attn.k_norm.weight": "transformer.h.{}.attn.norm_k.weight",
+        "model.layers.{}.self_attn.o_proj.weight": "transformer.h.{}.attn.proj.weight",
+        "model.layers.{}.post_attention_layernorm.weight": "transformer.h.{}.norm_2.weight",
+        "model.layers.{}.mlp.gate_proj.weight": "transformer.h.{}.mlp.fc_1.weight",
+        "model.layers.{}.mlp.up_proj.weight": "transformer.h.{}.mlp.fc_2.weight",
+        "model.layers.{}.mlp.down_proj.weight": "transformer.h.{}.mlp.proj.weight",
+        "model.norm.weight": "transformer.ln_f.weight",
+        "lm_head.weight": "lm_head.weight",
+    }
+
+    if progress_per_file is not None:
+        progress_per_file = progress_per_file / max(1, len(hf_weights) + len(qkv_weights))
+
+    for from_name, param in hf_weights.items():
+        name_template, *ids = layer_template(from_name, num_matches=2)
+        to_name = weight_map[name_template]
+        param = load_param(param, from_name, dtype, verbose=debug_mode)
+        if any(w in from_name for w in ("q_proj", "k_proj", "v_proj")):
+            qkv = qkv_weights.setdefault(ids[0], defaultdict(dict))
+            weight_name, weight_type = from_name.split(".")[-2:]
+            qkv[weight_type][weight_name] = param
+        if to_name is None:
+            continue
+        to_name = to_name.format(*ids)
+        if saver is not None:
+            param = saver.store_early(param)
+        state_dict[to_name] = param
+
+        if progress_per_file is not None:
+            pbar.update(progress_per_file)
+
+    if "lm_head.weight" not in state_dict:
+        state_dict["lm_head.weight"] = state_dict["transformer.wte.weight"]
+
+    for i in list(qkv_weights):
+        for weight_type in list(qkv_weights[i]):
+            qkv = qkv_weights[i][weight_type]
+            if len(qkv) != 3:
+                # qkv is split across different .bin files
+                continue
+            q = load_param(qkv["q_proj"], f"layer {i} q {weight_type}", dtype, verbose=debug_mode)
+            k = load_param(qkv["k_proj"], f"layer {i} k {weight_type}", dtype, verbose=debug_mode)
+            v = load_param(qkv["v_proj"], f"layer {i} v {weight_type}", dtype, verbose=debug_mode)
+            qkv = torch.cat((q, k, v))
+            state_dict[f"transformer.h.{i}.attn.qkv.{weight_type}"] = qkv
+            del qkv_weights[i][weight_type]
+
+            if progress_per_file is not None:
+                pbar.update(progress_per_file)
+
+
 def qkv_reassemble(
     param: Union[torch.Tensor, NotYetLoadedTensor], config: Config
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -624,6 +693,10 @@ def convert_hf_checkpoint(
         # holder to reconstitute the split q, k, v
         qkv_weights = {}
         copy_fn = partial(copy_weights_qwen_2_5, config, qkv_weights)
+    elif model_name.lower().startswith("qwen3"):
+        # holder to reconstitute the split q, k, v
+        qkv_weights = {}
+        copy_fn = partial(copy_weights_qwen_3, config, qkv_weights)
     elif config.mlp_class_name in ("LLaMAMLP", "GemmaMLP", "LLaMAMoE"):
         # holder to reconstitute the split q, k, v
         qkv_weights = {}

litgpt/scripts/convert_lit_checkpoint.py

@@ -393,6 +393,56 @@ def copy_weights_qwen_2_5(
             state_dict[to_name] = param
 
 
+def copy_weights_qwen_3(
+    config: Config,
+    state_dict: Dict[str, torch.Tensor],
+    lit_weights: Dict[str, Union[torch.Tensor, NotYetLoadedTensor]],
+    untie_weights: bool = False,
+    saver: Optional[incremental_save] = None,
+) -> None:
+    weight_map = {
+        "transformer.wte.weight": "model.embed_tokens.weight",
+        "transformer.h.{}.norm_1.weight": "model.layers.{}.input_layernorm.weight",
+        "transformer.h.{}.norm_2.weight": "model.layers.{}.post_attention_layernorm.weight",
+        "transformer.h.{}.attn.proj.weight": "model.layers.{}.self_attn.o_proj.weight",
+        "transformer.h.{}.attn.norm_q.weight": "model.layers.{}.self_attn.q_norm.weight",
+        "transformer.h.{}.attn.norm_k.weight": "model.layers.{}.self_attn.k_norm.weight",
+        "transformer.h.{}.mlp.fc_1.weight": "model.layers.{}.mlp.gate_proj.weight",
+        "transformer.h.{}.mlp.fc_2.weight": "model.layers.{}.mlp.up_proj.weight",
+        "transformer.h.{}.mlp.proj.weight": "model.layers.{}.mlp.down_proj.weight",
+        "transformer.ln_f.weight": "model.norm.weight",
+        "lm_head.weight": "lm_head.weight",
+    }
+
+    for from_name, param in lit_weights.items():
+        if from_name == "lm_head.weight" and untie_weights:
+            continue
+        name_template, *ids = layer_template(from_name, num_matches=2)
+        param = load_param(param, from_name, None)
+        if from_name.endswith(".attn.qkv.weight"):
+            weight_type = from_name.split(".")[-1]  # weight or bias
+            to_names = (
+                "model.layers.{}.self_attn.q_proj.{}".format(*ids, weight_type),
+                "model.layers.{}.self_attn.k_proj.{}".format(*ids, weight_type),
+                "model.layers.{}.self_attn.v_proj.{}".format(*ids, weight_type),
+            )
+            params = param.split(
+                (
+                    config.n_head * config.head_size,
+                    config.n_query_groups * config.head_size,
+                    config.n_query_groups * config.head_size,
+                )
+            )
+        else:
+            to_names = (weight_map[name_template].format(*ids),)
+            params = (param,)
+
+        for to_name, param in zip(to_names, params):
+            if saver is not None:
+                param = saver.store_early(param)
+            state_dict[to_name] = param
+
+
 def qkv_reassemble(param: Union[torch.Tensor, NotYetLoadedTensor], config: Config) -> torch.Tensor:
     """Reassemble from a normal to an interleaved placement in a QKV matrix.
     [Q, Q, ..., K, K, ..., V, V, ...] --> [Q, K, V, Q, K, V, ...]
@@ -437,6 +487,8 @@ def convert_lit_checkpoint(checkpoint_dir: Path, output_dir: Path) -> None:
         copy_fn = partial(copy_weights_phi, config)
     elif config.name.lower().startswith(("qwen2.5", "qwq")):
         copy_fn = partial(copy_weights_qwen_2_5, config)
+    elif config.name.lower().startswith("qwen3"):
+        copy_fn = partial(copy_weights_qwen_3, config)
     elif config.mlp_class_name in ("LLaMAMLP", "GemmaMLP", "LLaMAMoE"):
         untie_weights = "Gemma" in config.name
         copy_fn = partial(copy_weights_llama, config, untie_weights=untie_weights)