Upload 5 files

config.json

@@ -0,0 +1,97 @@
+{
+  "return_dict": true,
+  "output_hidden_states": false,
+  "torchscript": false,
+  "dtype": "float32",
+  "_output_attentions": false,
+  "pruned_heads": {},
+  "tie_word_embeddings": true,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "architectures": null,
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "task_specific_params": null,
+  "problem_type": null,
+  "tokenizer_class": null,
+  "prefix": null,
+  "bos_token_id": 151644,
+  "pad_token_id": 151643,
+  "eos_token_id": 151645,
+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
+  "num_beams": 1,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
+  "num_beam_groups": 1,
+  "diversity_penalty": 0.0,
+  "_name_or_path": "",
+  "_commit_hash": null,
+  "_attn_implementation_internal": "eager",
+  "transformers_version": null,
+  "tf_legacy_loss": false,
+  "use_bfloat16": false,
+  "dropout": 0.0,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "num_hidden_layers": 8,
+  "max_position_embeddings": 4096,
+  "vocab_size": 6400,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 50000.0,
+  "rope_scaling": null,
+  "self_distill": true,
+  "intermediate_size": 1536,
+  "expert_intermediate_size": 768,
+  "n_routed_experts": 0,
+  "moe_topk": 2,
+  "score_func": "softmax",
+  "n_shared_experts": 0,
+  "top_k_layer_dense": 8,
+  "aux_loss_alpha": 0.02,
+  "seq_aux": false,
+  "norm_topk_prob": true,
+  "noisy_expert": 0.0,
+  "moe_backend": "compact",
+  "router_bias_enabled": true,
+  "router_bias_update_rate": 0.001,
+  "router_bias_clamp": 5.0,
+  "num_heads": 6,
+  "mla_kv_lora_rank": 128,
+  "mla_qk_nope_head_dim": 64,
+  "mla_qk_rope_head_dim": 64,
+  "mla_attn_impl": "absorb",
+  "qkv_lora": false,
+  "torch_dtype": "bfloat16"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4447ad64478d7bdb64c55ade2a9a027b4d7c7ac8ac0420e26474e9f14755f795
+size 110141736

tokenizer_config.json

@@ -0,0 +1,335 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<|audio_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<|audio_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<|audio_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "17": {
+      "content": "<tts_pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "18": {
+      "content": "<tts_text_bos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "19": {
+      "content": "<tts_text_eod>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "20": {
+      "content": "<tts_text_bos_single>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "21": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "22": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "23": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "24": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "25": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "26": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "27": {
+      "content": "<|buffer1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "28": {
+      "content": "<|buffer2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "29": {
+      "content": "<|buffer3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "30": {
+      "content": "<|buffer4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "31": {
+      "content": "<|buffer5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "32": {
+      "content": "<|buffer6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "33": {
+      "content": "<|buffer7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "34": {
+      "content": "<|buffer8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "35": {
+      "content": "<|buffer9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>",
+    "<|audio_start|>",
+    "<|audio_end|>",
+    "<|audio_pad|>",
+    "<tts_pad>",
+    "<tts_text_bos>",
+    "<tts_text_eod>",
+    "<tts_text_bos_single>"
+  ],
+  "bos_token": "<|im_start|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "unk_token": "<|endoftext|>",
+  "image_token": "<|image_pad|>",
+  "audio_token": "<|audio_pad|>",
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>",
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "chat_template": "{%- if tools %}{{- '<|im_start|>system\\n' }}{%- if messages[0].role == 'system' %}{{- messages[0].content + '\\n\\n' }}{%- endif %}{{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}{%- for tool in tools %}{{- \"\\n\" }}{{- tool | tojson }}{%- endfor %}{{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}{%- else %}{%- if messages[0].role == 'system' %}{{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}{%- endif %}{%- endif %}{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}{%- for message in messages[::-1] %}{%- set index = (messages|length - 1) - loop.index0 %}{%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}{%- set ns.multi_step_tool = false %}{%- set ns.last_query_index = index %}{%- endif %}{%- endfor %}{%- for message in messages %}{%- if message.content is string %}{%- set content = message.content %}{%- else %}{%- set content = '' %}{%- endif %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}{{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}{%- elif message.role == \"assistant\" %}{%- set reasoning_content = '' %}{%- if message.reasoning_content is string %}{%- set reasoning_content = message.reasoning_content %}{%- else %}{%- if '</think>' in content %}{%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}{%- set content = content.split('</think>')[-1].lstrip('\\n') %}{%- endif %}{%- endif %}{%- if true %}{{- '<|im_start|>' + message.role + '\\n<think>' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}{%- endif %}{%- if message.tool_calls %}{%- for tool_call in message.tool_calls %}{%- if (loop.first and content) or (not loop.first) %}{{- '\\n' }}{%- endif %}{%- if tool_call.function %}{%- set tool_call = tool_call.function %}{%- endif %}{{- '<tool_call>\\n{\"name\": \"' }}{{- tool_call.name }}{{- '\", \"arguments\": ' }}{%- if tool_call.arguments is string %}{{- tool_call.arguments }}{%- else %}{{- tool_call.arguments | tojson }}{%- endif %}{{- '}\\n</tool_call>' }}{%- endfor %}{%- endif %}{{- '<|im_end|>\\n' }}{%- elif message.role == \"tool\" %}{%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}{{- '<|im_start|>user' }}{%- endif %}{{- '\\n<tool_response>\\n' }}{{- content }}{{- '\\n</tool_response>' }}{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}{{- '<|im_end|>\\n' }}{%- endif %}{%- endif %}{%- endfor %}{%- if add_generation_prompt %}{{- '<|im_start|>assistant\\n' }}{%- set juice_value = thinking_juice if thinking_juice is defined else 2.00 %}{%- set juice_str = '%.2f' | format(juice_value) %}{%- if open_thinking is defined and open_thinking %}{{- '<think>juice = ' + juice_str + '\\n' }}{%- else %}{{- '<think>juice = ' + juice_str + '\\n</think>\\n\\n' }}{%- endif %}{%- endif %}",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

ymodel3_eval.py

@@ -0,0 +1,755 @@
+"""Self-contained ymodel3 inference module.
+
+Only depends on: torch, safetensors.
+No dependency on kernel.*, model.ymodel3, transformers.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+from pathlib import Path
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from safetensors.torch import load_file as load_safetensors
+
+
+# ── Config ──────────────────────────────────────────────────────────
+
+
+class YConfig3:
+    model_type = "ynet3"
+
+    def __init__(self, **kwargs):
+        self.dropout = kwargs.get("dropout", 0.0)
+        self.bos_token_id = kwargs.get("bos_token_id", 151644)
+        self.eos_token_id = kwargs.get("eos_token_id", 151645)
+        self.pad_token_id = kwargs.get("pad_token_id", 151643)
+        self.hidden_act = kwargs.get("hidden_act", "silu")
+        self.hidden_size = kwargs.get("hidden_size", 768)
+        self.num_hidden_layers = kwargs.get("num_hidden_layers", 8)
+        self.max_position_embeddings = kwargs.get("max_position_embeddings", 8192)
+        self.vocab_size = kwargs.get("vocab_size", 6400)
+        self.rms_norm_eps = kwargs.get("rms_norm_eps", 1e-6)
+        self.rope_theta = kwargs.get("rope_theta", 5e4)
+        self.rope_scaling = kwargs.get("rope_scaling", None)
+        self.dtype = kwargs.get("dtype", "float32")
+        self.self_distill = kwargs.get("self_distill", True)
+        self.intermediate_size = kwargs.get("intermediate_size", 1536)
+        self.expert_intermediate_size = kwargs.get("expert_intermediate_size", None) or self.intermediate_size
+        self.n_routed_experts = kwargs.get("n_routed_experts", 0)
+        self.moe_topk = kwargs.get("moe_topk", 2)
+        self.score_func = kwargs.get("score_func", "softmax")
+        self.n_shared_experts = kwargs.get("n_shared_experts", 0)
+        self.top_k_layer_dense = kwargs.get("top_k_layer_dense", 1)
+        self.aux_loss_alpha = kwargs.get("aux_loss_alpha", 0.02)
+        self.seq_aux = kwargs.get("seq_aux", False)
+        self.norm_topk_prob = kwargs.get("norm_topk_prob", True)
+        self.noisy_expert = kwargs.get("noisy_expert", 0.0)
+        self.moe_backend = kwargs.get("moe_backend", "compact")
+        self.router_bias_enabled = kwargs.get("router_bias_enabled", True)
+        self.router_bias_update_rate = kwargs.get("router_bias_update_rate", 1e-3)
+        self.router_bias_clamp = kwargs.get("router_bias_clamp", 5.0)
+        self.num_heads = kwargs.get("num_heads", 12)
+        self.mla_kv_lora_rank = kwargs.get("mla_kv_lora_rank", 64)
+        self.mla_qk_nope_head_dim = kwargs.get("mla_qk_nope_head_dim", 64)
+        self.mla_qk_rope_head_dim = kwargs.get("mla_qk_rope_head_dim", 32)
+        self.mla_attn_impl = kwargs.get("mla_attn_impl", "absorb")
+        self.qkv_lora = kwargs.get("qkv_lora", False)
+
+    @property
+    def head_dim(self) -> int:
+        return self.mla_qk_nope_head_dim + self.mla_qk_rope_head_dim
+
+    def scale_lvl(self, lvl: int = 0):
+        if lvl == 0:
+            self.hidden_size = 1024
+            self.num_hidden_layers = 8
+            self.num_heads = 8
+            self.mla_kv_lora_rank = 256
+            self.mla_qk_nope_head_dim = 192
+            self.mla_qk_rope_head_dim = 64
+            self.intermediate_size = 2048
+            self.expert_intermediate_size = 512
+            self.n_routed_experts = 16
+            self.moe_topk = 1
+            self.n_shared_experts = 0
+            self.top_k_layer_dense = 1
+            self.router_bias_update_rate = 1e-3
+        elif lvl == -1:
+            self.hidden_size = 768
+            self.num_hidden_layers = 8
+            self.num_heads = 6
+            self.mla_kv_lora_rank = 128
+            self.mla_qk_nope_head_dim = 64
+            self.mla_qk_rope_head_dim = 64
+            self.intermediate_size = 1536
+            self.expert_intermediate_size = 768
+            self.n_routed_experts = 0
+            self.moe_topk = 2
+            self.n_shared_experts = 0
+            self.top_k_layer_dense = 8
+        elif lvl == -2:
+            self.hidden_size = 512
+            self.num_hidden_layers = 4
+            self.num_heads = 4
+            self.mla_kv_lora_rank = 128
+            self.mla_qk_nope_head_dim = 64
+            self.mla_qk_rope_head_dim = 32
+            self.intermediate_size = 1024
+            self.expert_intermediate_size = 512
+            self.n_routed_experts = 0
+            self.moe_topk = 2
+            self.n_shared_experts = 0
+            self.top_k_layer_dense = 4
+        else:
+            raise ValueError(f"invalid ymodel3 scale level: {lvl}")
+        return self
+
+    @classmethod
+    def from_json_file(cls, path: str) -> "YConfig3":
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        return cls(**data)
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "YConfig3":
+        return cls(**data)
+
+
+# ── Basic modules ──────────────────────────────────────────────────
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = x.float() * torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)
+        return (out * self.weight.float()).to(x.dtype)
+
+
+class SEBlock(nn.Module):
+    def __init__(self, dim: int, reduction: int = 16, act: Optional[nn.Module] = None):
+        super().__init__()
+        reduction = max(reduction, dim // reduction)
+        self.se = nn.Sequential(
+            nn.Linear(dim, reduction, bias=False),
+            act or nn.SiLU(),
+            nn.Linear(reduction, dim, bias=False),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x * self.se(x)
+
+
+# ── RoPE helpers ──────────────────────────────────────────────────
+
+
+def _yarn_linear_ramp(low: float, high: float, dim: int) -> torch.Tensor:
+    if low == high:
+        high += 0.001
+    linear = (torch.arange(dim, dtype=torch.float32) - low) / (high - low)
+    return torch.clamp(linear, 0.0, 1.0)
+
+
+def _yarn_correction_dim(num_rotations: float, dim: int, theta: float, max_position_embeddings: int) -> float:
+    return dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi)) / (2 * math.log(theta))
+
+
+def precompute_freqs_cis(
+    dim: int,
+    end: int,
+    theta: float,
+    rope_scaling: Optional[dict] = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
+    attention_factor = 1.0
+    if rope_scaling and str(rope_scaling.get("type", "yarn")).lower() == "yarn":
+        factor = float(rope_scaling.get("factor", 1.0))
+        if factor > 1.0:
+            original = int(rope_scaling.get("original_max_position_embeddings", end))
+            beta_fast = float(rope_scaling.get("beta_fast", 32.0))
+            beta_slow = float(rope_scaling.get("beta_slow", 1.0))
+            low = math.floor(_yarn_correction_dim(beta_fast, dim, theta, original))
+            high = math.ceil(_yarn_correction_dim(beta_slow, dim, theta, original))
+            ramp = _yarn_linear_ramp(low, high, dim // 2)
+            freqs = freqs / factor * (1.0 - ramp) + freqs * ramp
+            attention_factor = float(rope_scaling.get("attention_factor", 1.0))
+    t = torch.arange(end)
+    freqs = torch.outer(t, freqs).float()
+    freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) * attention_factor
+    freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) * attention_factor
+    return freqs_cos, freqs_sin
+
+
+def rotate_half(x: torch.Tensor) -> torch.Tensor:
+    return torch.cat((-x[..., x.shape[-1] // 2 :], x[..., : x.shape[-1] // 2]), dim=-1)
+
+
+def apply_rope_to_single(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+    if cos.dim() == 2:
+        cos = cos.unsqueeze(0).unsqueeze(0)
+        sin = sin.unsqueeze(0).unsqueeze(0)
+    elif cos.dim() == 3:
+        cos = cos.unsqueeze(1)
+        sin = sin.unsqueeze(1)
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+# ── Attention ──────────────────────────────────────────────────────
+
+
+class MLGA(nn.Module):
+    """Multihead Latent Gated Attention"""
+
+    def __init__(self, config: YConfig3, layer_id: int):
+        super().__init__()
+        self.layer_id = layer_id
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_heads
+        self.dropout = config.dropout
+        self.kv_lora_rank = config.mla_kv_lora_rank
+        self.qk_nope_head_dim = config.mla_qk_nope_head_dim
+        self.qk_rope_head_dim = config.mla_qk_rope_head_dim
+        self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
+        self.attn_impl = config.mla_attn_impl
+        self.softmax_scale = self.qk_head_dim ** -0.5
+        self.out_dim = self.num_heads * self.kv_lora_rank
+
+        self.wq = nn.Linear(self.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
+        self.wkv_a = nn.Linear(self.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=False)
+        self.kv_norm = RMSNorm(self.kv_lora_rank, config.rms_norm_eps)
+        self.wkv_b = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim, bias=False)
+        self.z_proj = nn.Linear(self.hidden_size, self.out_dim, bias=False)
+        self.o_proj = nn.Linear(self.out_dim, self.hidden_size, bias=False)
+
+    def _project_q(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        bsz, seq_len, _ = x.shape
+        q = self.wq(x)
+        q = q.reshape(bsz, seq_len, self.num_heads, self.qk_head_dim)
+        return q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+    def _project_kv(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        raw = self.wkv_a(x)
+        c_kv, k_pe = raw.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+        c_kv = self.kv_norm(c_kv)
+        k_pe = apply_rope_to_single(k_pe.unsqueeze(1), cos, sin).permute(0, 2, 1, 3)
+        return c_kv, k_pe
+
+    def _explicit_kv(self, c_kv: torch.Tensor, k_pe: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        bsz, seq_len, _ = c_kv.shape
+        k_nope = self.wkv_b(c_kv).reshape(bsz, seq_len, self.num_heads, self.qk_nope_head_dim)
+        k = torch.cat([k_nope, k_pe.expand(-1, -1, self.num_heads, -1)], dim=-1)
+        v = c_kv.unsqueeze(2).expand(-1, -1, self.num_heads, -1)
+        return k, v
+
+    def _attention_mask(self, attention_mask: Optional[torch.Tensor], bsz: int, seq_len: int, total_len: int):
+        if attention_mask is None:
+            return None
+        if attention_mask.shape[-1] != total_len:
+            attention_mask = attention_mask[..., -total_len:]
+        mask = attention_mask.reshape(bsz, 1, 1, total_len).bool()
+        return mask.expand(bsz, self.num_heads, seq_len, total_len)
+
+    def _forward_sdpa(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        c_kv: torch.Tensor,
+        k_pe: torch.Tensor,
+        z: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        bsz, seq_len, _, _ = q_nope.shape
+        total_len = c_kv.shape[1]
+        k, v = self._explicit_kv(c_kv, k_pe)
+        q = torch.cat([q_nope, q_pe], dim=-1).permute(0, 2, 1, 3)
+        k = k.permute(0, 2, 1, 3)
+        v = v.permute(0, 2, 1, 3)
+        attn_mask = self._attention_mask(attention_mask, bsz, seq_len, total_len)
+        is_causal = attention_mask is None and seq_len == total_len
+        out = F.scaled_dot_product_attention(
+            q, k, v,
+            attn_mask=attn_mask,
+            dropout_p=self.dropout if self.training else 0.0,
+            is_causal=is_causal,
+            scale=self.softmax_scale,
+        )
+        out = out.permute(0, 2, 1, 3).reshape(bsz, seq_len, self.out_dim)
+        out = out * torch.sigmoid(z)
+        return self.o_proj(out)
+
+    def _forward_absorb(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        c_kv: torch.Tensor,
+        k_pe: torch.Tensor,
+        z: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        bsz, seq_len, _, _ = q_nope.shape
+        total_len = c_kv.shape[1]
+        w = self.wkv_b.weight.reshape(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
+        q_nope_c = torch.einsum("bshd,hdc->bshc", q_nope, w)
+        scores = torch.einsum("bshc,btc->bsht", q_nope_c, c_kv)
+        scores = scores + torch.einsum("bshr,btr->bsht", q_pe, k_pe.squeeze(2))
+        scores = scores * self.softmax_scale
+
+        causal = torch.full((seq_len, seq_len), float("-inf"), device=scores.device, dtype=scores.dtype)
+        causal = torch.triu(causal, diagonal=1).reshape(1, seq_len, 1, seq_len)
+        scores = scores + F.pad(causal, (total_len - seq_len, 0), value=0.0)
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != total_len:
+                attention_mask = attention_mask[..., -total_len:]
+            scores = scores + (1.0 - attention_mask.reshape(bsz, 1, 1, total_len).float()) * -1e9
+        probs = torch.softmax(scores.float(), dim=-1).to(q_nope.dtype)
+        out = torch.einsum("bsht,btc->bshc", probs, c_kv).reshape(bsz, seq_len, self.out_dim)
+        out = out * torch.sigmoid(z)
+        return self.o_proj(out)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        past_key_values: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor, torch.Tensor]]]:
+        bsz, seq_len, _ = x.shape
+        cos, sin = position_embeddings
+        if cos.dim() == 2:
+            cos = cos[:seq_len, : self.qk_rope_head_dim]
+            sin = sin[:seq_len, : self.qk_rope_head_dim]
+        else:
+            cos = cos[:, :seq_len, : self.qk_rope_head_dim]
+            sin = sin[:, :seq_len, : self.qk_rope_head_dim]
+        q_nope, q_pe = self._project_q(x)
+        q_pe = apply_rope_to_single(q_pe.permute(0, 2, 1, 3), cos, sin).permute(0, 2, 1, 3)
+        c_kv, k_pe = self._project_kv(x, cos, sin)
+        z = self.z_proj(x)
+
+        if past_key_values is not None:
+            past_c, past_pe = past_key_values
+            c_kv = torch.cat([past_c, c_kv], dim=1)
+            k_pe = torch.cat([past_pe, k_pe], dim=1)
+        new_past = (c_kv, k_pe) if use_cache else None
+
+        if self.attn_impl == "naive":
+            out = self._forward_sdpa(q_nope, q_pe, c_kv, k_pe, z, attention_mask)
+        else:
+            out = self._forward_absorb(q_nope, q_pe, c_kv, k_pe, z, attention_mask)
+        out = F.dropout(out, p=self.dropout, training=self.training)
+        return out, new_past
+
+
+# ── FFN / MoE ──────────────────────────────────────────────────────
+
+
+_ACT_FNS = {
+    "silu": F.silu,
+    "swish": F.silu,
+    "relu": F.relu,
+    "gelu": lambda x: F.gelu(x, approximate="tanh"),
+    "sigmoid": torch.sigmoid,
+}
+
+_ACT_MODULES = {
+    "silu": nn.SiLU,
+    "swish": nn.SiLU,
+    "relu": nn.ReLU,
+    "gelu": lambda: nn.GELU(approximate="tanh"),
+    "sigmoid": nn.Sigmoid,
+}
+
+
+class DenseFFN(nn.Module):
+    def __init__(self, config: YConfig3, intermediate_size: Optional[int] = None):
+        super().__init__()
+        inter = intermediate_size or config.intermediate_size
+        self.up_proj = nn.Linear(config.hidden_size, inter, bias=False)
+        self.gate_proj = nn.Linear(config.hidden_size, inter, bias=False)
+        self.down_proj = nn.Linear(inter, config.hidden_size, bias=False)
+        self.hidden_act = config.hidden_act
+        self.act = _ACT_FNS.get(config.hidden_act, F.silu)
+        self.dropout = config.dropout
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        up, gate = self.up_proj(x), self.gate_proj(x)
+        up = self.act(gate) * up
+        up = F.dropout(up, p=self.dropout, training=self.training)
+        return self.down_proj(up)
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: YConfig3):
+        super().__init__()
+        self.n_routed_experts = config.n_routed_experts
+        self.topk = min(config.moe_topk, max(1, config.n_routed_experts))
+        self.score_func = config.score_func
+        self.norm_topk_prob = config.norm_topk_prob
+        self.aux_loss_alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+        self.router_bias_enabled = config.router_bias_enabled
+        self.router_bias_update_rate = config.router_bias_update_rate
+        self.router_bias_clamp = config.router_bias_clamp
+        self.weight = nn.Linear(int(config.hidden_size), int(self.n_routed_experts), bias=False)
+        if self.router_bias_enabled:
+            self.register_buffer("router_bias", torch.zeros(self.n_routed_experts), persistent=True)
+        else:
+            self.register_buffer("router_bias", None, persistent=False)
+
+    def forward(self, x: torch.Tensor, aux_mask: Optional[torch.Tensor] = None):
+        bsz, seq_len, hidden = x.shape
+        flat = x.reshape(-1, hidden)
+        route_logits = self.weight(flat)
+        if self.score_func == "softmax":
+            route_scores = torch.softmax(route_logits.float(), dim=-1).to(x.dtype)
+        elif self.score_func == "sigmoid":
+            route_scores = torch.sigmoid(route_logits.float()).to(x.dtype)
+        else:
+            raise ValueError(f"unsupported MoE score_func: {self.score_func}")
+
+        choice_scores = route_scores
+        if self.router_bias is not None:
+            choice_scores = choice_scores + self.router_bias.to(dtype=choice_scores.dtype).unsqueeze(0)
+
+        topk_idx = torch.topk(choice_scores, k=self.topk, dim=-1, sorted=False).indices
+        topk_weight = route_scores.gather(1, topk_idx)
+        if self.topk > 1 and self.norm_topk_prob:
+            denom = topk_weight.float().sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = (topk_weight.float() / denom).to(x.dtype)
+
+        aux_loss = x.new_zeros((), dtype=x.dtype)
+        return (
+            topk_idx.reshape(bsz, seq_len, self.topk),
+            topk_weight.reshape(bsz, seq_len, self.topk),
+            aux_loss,
+        )
+
+
+def _torch_moe_swiglu(
+    x: torch.Tensor,
+    topk_idx: torch.Tensor,
+    topk_weight: torch.Tensor,
+    w_up: torch.Tensor,
+    w_down: torch.Tensor,
+    activation: str = "silu",
+) -> torch.Tensor:
+    """Pure PyTorch MoE SwiGLU forward (inference only, no noisy_expert)."""
+    original_shape = x.shape
+    x_flat = x.reshape(-1, x.shape[-1])
+    idx = topk_idx.reshape(x_flat.shape[0], -1)
+    weight = topk_weight.reshape(x_flat.shape[0], -1)
+    y = torch.zeros_like(x_flat)
+    n_experts = w_up.shape[0]
+    inter = w_down.shape[-1]
+    act_fn = _ACT_FNS.get(activation, F.silu)
+    for expert_id in range(n_experts):
+        token_pos, choice_pos = torch.where(idx == expert_id)
+        if token_pos.numel() == 0:
+            continue
+        inp = x_flat[token_pos]
+        uv = F.linear(inp, w_up[expert_id])
+        up, gate = uv.split(inter, dim=-1)
+        hidden = act_fn(gate) * up
+        out = F.linear(hidden, w_down[expert_id])
+        route_w = weight[token_pos, choice_pos].unsqueeze(-1)
+        y.index_add_(0, token_pos, out * route_w)
+    return y.reshape(original_shape)
+
+
+class YMoE(nn.Module):
+    """Pure PyTorch eval MoE (no Triton dependency)."""
+
+    def __init__(self, config: YConfig3, layer_id: int):
+        super().__init__()
+        self.layer_id = layer_id
+        self.hidden_size = config.hidden_size
+        self.expert_intermediate_size = config.expert_intermediate_size
+        self.intermediate_size = self.expert_intermediate_size
+        self.n_routed_experts = config.n_routed_experts
+        self.use_moe = self.n_routed_experts > 0 and layer_id >= config.top_k_layer_dense
+        self.noisy_expert = config.noisy_expert
+        if not self.use_moe:
+            self.dense = DenseFFN(config)
+            self.gate = None
+            self.w_up = None
+            self.w_down = None
+            return
+        self.dense = None
+        self.gate = MoEGate(config)
+        self.w_up = nn.Parameter(torch.empty(self.n_routed_experts, 2 * self.expert_intermediate_size, self.hidden_size))
+        self.w_down = nn.Parameter(torch.empty(self.n_routed_experts, self.hidden_size, self.expert_intermediate_size))
+        nn.init.kaiming_uniform_(self.w_up, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.w_down, a=math.sqrt(5))
+
+    def forward(self, x: torch.Tensor, aux_mask: Optional[torch.Tensor] = None):
+        if not self.use_moe:
+            return self.dense(x), None
+        topk_idx, topk_weight, aux_loss = self.gate(x, aux_mask)
+        y = _torch_moe_swiglu(x, topk_idx, topk_weight, self.w_up, self.w_down, activation="silu")
+        return y, aux_loss
+
+
+# ── Transformer block ──────────────────────────────────────────────
+
+
+class YBlock3(nn.Module):
+    def __init__(self, config: YConfig3, layer_id: int):
+        super().__init__()
+        self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.attn = MLGA(config, layer_id)
+        self.ffn = YMoE(config, layer_id)
+        act_module = _ACT_MODULES.get(config.hidden_act, nn.SiLU)
+        self.se1 = SEBlock(config.hidden_size, act=act_module() if isinstance(act_module, type) else act_module())
+        self.se2 = SEBlock(config.hidden_size, act=nn.SiLU())
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        past_key_values=None,
+        use_cache: bool = False,
+        attention_mask: Optional[torch.Tensor] = None,
+        aux_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        x0 = self.se1(self.input_layernorm(x))
+        attn_out, past = self.attn(
+            x0,
+            position_embeddings,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            use_cache=use_cache,
+        )
+        x = x + attn_out
+        x0 = self.se2(self.post_attention_layernorm(x))
+        ffn_out, aux_loss = self.ffn(x0, aux_mask)
+        x = x + ffn_out
+        return x, past, aux_loss
+
+
+# ── Full model ────────────────────────────────────────────────────
+
+
+class YModel3(nn.Module):
+    def __init__(self, config: YConfig3):
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.num_layers = config.num_hidden_layers
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.dropout = config.dropout
+        self.use_self_distill = config.self_distill
+        self.layers = nn.ModuleList([YBlock3(config, i) for i in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        freqs_cos, freqs_sin = precompute_freqs_cis(
+            dim=config.mla_qk_rope_head_dim,
+            end=config.max_position_embeddings,
+            theta=config.rope_theta,
+            rope_scaling=config.rope_scaling,
+        )
+        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
+        self.register_buffer("freqs_sin", freqs_sin, persistent=False)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[list] = None,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        bsz, seq_len = input_ids.shape
+        if use_cache and past_key_values is None:
+            past_key_values = [None] * self.num_layers
+        if cache_position is None:
+            if past_key_values is not None and past_key_values[0] is not None:
+                past_seen = past_key_values[0][0].shape[1]
+            else:
+                past_seen = 0
+            cache_position = torch.arange(past_seen, past_seen + seq_len, device=input_ids.device)
+
+        x = F.dropout(self.embed_tokens(input_ids), p=self.dropout, training=self.training)
+        if position_ids is None:
+            position_ids = cache_position
+        position_embeddings = (self.freqs_cos[position_ids].to(x.device), self.freqs_sin[position_ids].to(x.device))
+        aux_mask = None
+        new_past = [] if use_cache else None
+        aux_loss = None
+
+        for i, layer in enumerate(self.layers):
+            past = past_key_values[i] if past_key_values is not None else None
+            x, layer_past, layer_aux = layer(
+                x,
+                position_embeddings=position_embeddings,
+                past_key_values=past,
+                attention_mask=attention_mask,
+                use_cache=use_cache,
+                aux_mask=aux_mask,
+            )
+            if use_cache:
+                new_past.append(layer_past)
+            if self.training and layer_aux is not None:
+                aux_loss = layer_aux if aux_loss is None else aux_loss + layer_aux
+
+        return self.norm(x), new_past, None, aux_loss
+
+
+class _InferenceOutput:
+    """Simple container for model outputs (replaces transformers CausalLMOutputWithPast)."""
+
+    __slots__ = ("last_hidden_state", "logits", "past_key_values", "dist_loss", "aux_loss")
+
+    def __init__(self):
+        self.last_hidden_state = None
+        self.logits = None
+        self.past_key_values = None
+        self.dist_loss = None
+        self.aux_loss = None
+
+    def __setitem__(self, key, value):
+        setattr(self, key, value)
+
+
+class YForCausalLM3(nn.Module):
+    """Pure PyTorch CausalLM wrapper for ymodel3 inference (no transformers dependency)."""
+
+    config_class = YConfig3
+
+    def __init__(self, config: Optional[YConfig3] = None):
+        super().__init__()
+        self.config = config or YConfig3()
+        self.model = YModel3(self.config)
+        self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        self.model.embed_tokens.weight = self.lm_head.weight
+        self.OUT = _InferenceOutput()
+        dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32}.get(self.config.dtype)
+        if dtype is not None:
+            self.to(dtype)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[list] = None,
+        use_cache: bool = False,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        h, past_kvs, dist_loss, aux_loss = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_ids=kwargs.get("position_ids", None),
+        )
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(h[:, slice_indices, :])
+        self.OUT.__setitem__("last_hidden_state", h)
+        self.OUT.__setitem__("logits", logits)
+        self.OUT.__setitem__("past_key_values", past_kvs)
+        self.OUT.__setitem__("dist_loss", dist_loss)
+        self.OUT.__setitem__("aux_loss", aux_loss)
+        return self.OUT
+
+    def generate(
+        self,
+        inputs,
+        attention_mask=None,
+        max_new_tokens=8192,
+        temperature=0.85,
+        top_p=0.85,
+        top_k=50,
+        eos_token_id=None,
+        streamer=None,
+        use_cache=True,
+        num_return_sequences=1,
+        do_sample=True,
+        repetition_penalty=1.0,
+        **kwargs,
+    ):
+        input_ids = kwargs.get("input_ids", inputs).repeat(num_return_sequences, 1)
+        attention_mask = attention_mask.repeat(num_return_sequences, 1) if attention_mask is not None else None
+        past_key_values = None
+        if streamer:
+            streamer.put(input_ids.cpu())
+        with torch.no_grad():
+            for _ in range(max_new_tokens):
+                if use_cache and past_key_values is not None:
+                    outputs = self.forward(input_ids[:, -1:], None, past_key_values, use_cache=use_cache)
+                else:
+                    outputs = self.forward(input_ids, attention_mask, past_key_values, use_cache=use_cache)
+                logits = outputs.logits[:, -1, :] / temperature
+                if repetition_penalty != 1.0:
+                    for i in range(input_ids.shape[0]):
+                        logits[i, torch.unique(input_ids[i])] /= repetition_penalty
+                if top_k > 0:
+                    logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float("inf")
+                if top_p < 1.0:
+                    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                    mask = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) > top_p
+                    mask[..., 1:], mask[..., 0] = mask[..., :-1].clone(), 0
+                    logits[mask.scatter(1, sorted_indices, mask)] = -float("inf")
+                next_token = torch.multinomial(torch.softmax(logits, dim=-1), 1) if do_sample else torch.argmax(logits, dim=-1, keepdim=True)
+                input_ids = torch.cat([input_ids, next_token], dim=-1)
+                past_key_values = outputs.past_key_values if use_cache else None
+                if streamer:
+                    streamer.put(next_token.cpu())
+                if eos_token_id and (next_token == eos_token_id).any():
+                    break
+        if streamer:
+            streamer.end()
+        return input_ids
+
+
+# ── Loading utilities ──────────────────────────────────────────────
+
+
+def _load_state_dict(path: Union[str, Path]) -> dict[str, torch.Tensor]:
+    path = Path(path)
+    if path.is_dir():
+        safetensors_path = path / "model.safetensors"
+        bin_path = path / "pytorch_model.bin"
+        if safetensors_path.exists():
+            path = safetensors_path
+        elif bin_path.exists():
+            path = bin_path
+        else:
+            raise FileNotFoundError(f"no model.safetensors or pytorch_model.bin found in {path}")
+    if path.suffix == ".safetensors":
+        return load_safetensors(str(path), device="cpu")
+    return torch.load(path, map_location="cpu", weights_only=True)
+
+
+def load_ymodel3_eval(path: Union[str, Path], config: Optional[YConfig3] = None, strict: bool = True) -> YForCausalLM3:
+    if config is None:
+        config_path = Path(path) / "config.json" if Path(path).is_dir() else Path(path).with_name("config.json")
+        if not config_path.exists():
+            raise FileNotFoundError("config is required when config.json is not next to the checkpoint")
+        config = YConfig3.from_json_file(str(config_path))
+    model = YForCausalLM3(config)
+    state = _load_state_dict(path)
+    model.load_state_dict(state, strict=strict)
+    model.eval()
+    return model
+
+
+# ── Backward-compatible aliases ────────────────────────────────────
+
+YModel3Eval = YModel3
+YForCausalLM3Eval = YForCausalLM3