Instructions to use SnifferCaptain/ymodel3.1-200m-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SnifferCaptain/ymodel3.1-200m-pt with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SnifferCaptain/ymodel3.1-200m-pt", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SnifferCaptain/ymodel3.1-200m-pt", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SnifferCaptain/ymodel3.1-200m-pt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SnifferCaptain/ymodel3.1-200m-pt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SnifferCaptain/ymodel3.1-200m-pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SnifferCaptain/ymodel3.1-200m-pt

SGLang

How to use SnifferCaptain/ymodel3.1-200m-pt with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SnifferCaptain/ymodel3.1-200m-pt" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SnifferCaptain/ymodel3.1-200m-pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SnifferCaptain/ymodel3.1-200m-pt" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SnifferCaptain/ymodel3.1-200m-pt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SnifferCaptain/ymodel3.1-200m-pt with Docker Model Runner:
```
docker model run hf.co/SnifferCaptain/ymodel3.1-200m-pt
```

SnifferCaptain commited on Jul 8

Commit

5f26b65

verified ·

1 Parent(s): 8583aaf

init

Browse files

Files changed (9) hide show

README.md +49 -0
__init__.py +1 -0
config.json +95 -0
configuration_ymodel31.py +40 -0
model.safetensors +3 -0
modeling_ymodel31.py +79 -0
tokenizer.json +0 -0
tokenizer_config.json +335 -0
ymodel31_eval.py +689 -0

README.md CHANGED Viewed

@@ -1,3 +1,52 @@
 ---
 license: mit
 ---

 ---
 license: mit
+language:
+  - zh
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - custom_code
+  - safetensors
+  - ymodel
+  - ymodel31
 ---
+# ymodel3.1-200M
+`ymodel3.1-200M`是SnifferCaptain训练的到目前为止（7/7/2026）最新、最大的语言模型。采用ynet3.1架构，在ymodel3所使用的ynet3架构上添加了Sengram记忆模块，其模型容量上相比上一代架构有一定的提升。
+## 模型细节
+- 在ynet3的基础上，添加了Sengram记忆模块，这种稀疏嵌入可以在增大模型参数量的时候，尽可能减少模型的额外计算量。
+- Sengram （Simplified Engram（ https://arxiv.org/abs/2601.07372 ））记忆模块对原始的Engram基于n-gram的记忆模块进行了简化，分为掌管稀疏路由的SengramIndexer与掌管嵌入的SengramPLE。
+- 在SengramIndexer记忆选择过程中，相比原来的Engram，它将记忆的选择从简单快速的哈希改为在语义空间内线性投影，而后使用类似MoE的softmax + topk让后续嵌入在语义空间中得到连续的表示。其优点在于它将离散的可碰撞的哈希嵌入转换为在语义空间中连续的嵌入方式，且在建模上不局限于n-gram这种仅语言模型支持的特殊数据，它天生支持多模态。其缺点在于相比简单哈希计算量会大很多，但是整体上来说由于所有层共享一个选择结果，占用模型推理的总计算量很小。
+- SengramPLE的意思是Sengram模块的逐层嵌入(Per-Layer Embedding)，模块接收来自Indexer的topk与score输出，在每层独立的分桶内取得逐层嵌入并加权求和。SengramPLE去掉了原本engram对嵌入的gate，选择了直接相加的方式加速计算。
+| 键 | 值 |
+| --- | --- |
+| 架构 | YModel3.1 |
+| 参数数量 | 210.22M |
+| 层数 | 12 |
+| 隐藏层维度 | 768 |
+| 词表大小 | 6400 |
+| 注意力头数 | 8 |
+| 注意力头维度 | 192 |
+| KV嵌入维度 | 256 |
+| RoPE嵌入维度 | 64 |
+| FFN激活函数 | SwiGLU |
+| FFN中间层大小 | 2048 |
+| 归一化 | RMSNorm + SEBlock |
+| 注意力机制 | MLGA |
+| Sengram桶大小 | 8192 |
+| Sengram选择数量 | 8 |
+| Sengram评分函数 | Softmax |
+| 数据类型 | bfloat16 |
+## 训练细节
+- 在预训练阶段，模型在512长度的上下文充分使用**5B tokens**，1e-4带warmup的余弦退火到1e-5的学习率下，完成预训练。最终的ppl为5.041（6400BPE词表长度）
+- 模型全程采用与YModel3相同的**SiMuon优化器**训练
+- 模型的tokenlizer与词嵌入层使用的是预训练权重，来自MiniMind3-v（ https://github.com/jingyaogong/minimind ）
+- 预训练阶段采用的批次大小为前50% 64k tokens/step、后50% 128k tokens/step

__init__.py ADDED Viewed

	@@ -0,0 +1 @@


1	+ from .modeling_ymodel31 import YForCausalLM31

config.json ADDED Viewed

	@@ -0,0 +1,95 @@

+{
+  "return_dict": true,
+  "output_hidden_states": false,
+  "torchscript": false,
+  "dtype": "bfloat16",
+  "_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": [
+    "YForCausalLM31"
+  ],
+  "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": 12,
+  "max_position_embeddings": 8192,
+  "vocab_size": 6400,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 50000.0,
+  "rope_scaling": null,
+  "self_distill": true,
+  "intermediate_size": 2048,
+  "num_heads": 8,
+  "mla_kv_lora_rank": 256,
+  "mla_qk_nope_head_dim": 128,
+  "mla_qk_rope_head_dim": 64,
+  "mla_attn_impl": "absorb",
+  "qkv_lora": false,
+  "gradient_checkpointing": 0,
+  "use_sengram": true,
+  "sengram_bucket_size": 8192,
+  "sengram_topk": 8,
+  "engram_bucket_size": 4096,
+  "engram_topk": 2,
+  "model_type": "ynet31",
+  "auto_map": {
+    "AutoConfig": "configuration_ymodel31.YConfig31",
+    "AutoModelForCausalLM": "modeling_ymodel31.YForCausalLM31"
+  }
+}

configuration_ymodel31.py ADDED Viewed

	@@ -0,0 +1,40 @@

+from transformers import PretrainedConfig
+class YConfig31(PretrainedConfig):
+    model_type = "ynet31"
+    def __init__(self, **kwargs):
+        self.dropout = kwargs.pop("dropout", 0.0)
+        self.bos_token_id = kwargs.pop("bos_token_id", 151644)
+        self.eos_token_id = kwargs.pop("eos_token_id", 151645)
+        self.pad_token_id = kwargs.pop("pad_token_id", 151643)
+        self.hidden_act = kwargs.pop("hidden_act", "silu")
+        self.hidden_size = kwargs.pop("hidden_size", 768)
+        self.num_hidden_layers = kwargs.pop("num_hidden_layers", 8)
+        self.max_position_embeddings = kwargs.pop("max_position_embeddings", 8192)
+        self.vocab_size = kwargs.pop("vocab_size", 6400)
+        self.rms_norm_eps = kwargs.pop("rms_norm_eps", 1e-6)
+        self.rope_theta = kwargs.pop("rope_theta", 5e4)
+        self.rope_scaling = kwargs.pop("rope_scaling", None)
+        self.dtype = kwargs.pop("dtype", "float32")
+        self.self_distill = kwargs.pop("self_distill", True)
+        self.intermediate_size = kwargs.pop("intermediate_size", 1536)
+        self.num_heads = kwargs.pop("num_heads", 12)
+        self.mla_kv_lora_rank = kwargs.pop("mla_kv_lora_rank", 64)
+        self.mla_qk_nope_head_dim = kwargs.pop("mla_qk_nope_head_dim", 64)
+        self.mla_qk_rope_head_dim = kwargs.pop("mla_qk_rope_head_dim", 32)
+        self.mla_attn_impl = kwargs.pop("mla_attn_impl", "absorb")
+        self.qkv_lora = kwargs.pop("qkv_lora", False)
+        self.gradient_checkpointing = kwargs.pop("gradient_checkpointing", 0)
+        self.use_sengram = kwargs.pop("use_sengram", True)
+        self.sengram_bucket_size = kwargs.pop("sengram_bucket_size", 4096)
+        self.sengram_topk = kwargs.pop("sengram_topk", 2)
+        self.engram_bucket_size = kwargs.pop("engram_bucket_size", self.sengram_bucket_size)
+        self.engram_topk = kwargs.pop("engram_topk", self.sengram_topk)
+        super().__init__(
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+            **kwargs,
+        )

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:4163a5a055327983c2207236968d7b2e7a75eb3018082320d3606c2c7feb627c
+size 442887840

modeling_ymodel31.py ADDED Viewed

	@@ -0,0 +1,79 @@

+from __future__ import annotations
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from .configuration_ymodel31 import YConfig31
+from .ymodel31_eval import YModel31
+class YForCausalLM31(PreTrainedModel):
+    config_class = YConfig31
+    base_model_prefix = "model"
+    def __init__(self, config: YConfig31):
+        super().__init__(config)
+        self.model = YModel31(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.model.embed_tokens.weight = self.lm_head.weight
+        self.post_init()
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+        self.lm_head.weight = value.weight
+    def get_output_embeddings(self):
+        return self.lm_head
+    def tie_weights(self):
+        self.model.embed_tokens.weight = self.lm_head.weight
+        return None
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "attention_mask": attention_mask,
+            "use_cache": use_cache,
+            "cache_position": kwargs.get("cache_position", None),
+            "position_ids": kwargs.get("position_ids", None),
+        }
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        past_key_values=None,
+        use_cache=False,
+        cache_position=None,
+        position_ids=None,
+        **kwargs,
+    ):
+        h, past_kvs = 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=position_ids,
+        )
+        logits = self.lm_head(h)
+        return CausalLMOutputWithPast(
+            logits=logits,
+            past_key_values=past_kvs,
+            hidden_states=(h,),
+        )

tokenizer.json ADDED Viewed

The diff for this file is too large to render. See raw diff

tokenizer_config.json ADDED Viewed

	@@ -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
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+      "special": true
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+    "7": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
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+      "special": true
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+      "special": true
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+      "content": "<|vision_pad|>",
+      "lstrip": false,
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+      "content": "<|image_pad|>",
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+      "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": 262144,
+  "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 %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- 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>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if true %}\n            {{- '<|im_start|>' + message.role + '\\n<think>' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if open_thinking is defined and open_thinking is true %}\n        {{- '<think>' }}\n    {%- else %}\n        {{- '<think>\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

ymodel31_eval.py ADDED Viewed

	@@ -0,0 +1,689 @@

+"""Standalone evaluation/inference implementation for ymodel31.
+This file intentionally contains a self-contained inference path so exported
+checkpoints can be loaded without importing the training implementation.
+Training-only features such as gradient checkpointing and self-distillation are
+omitted here on purpose.
+"""
+from __future__ import annotations
+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
+from transformers import GenerationMixin, PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_outputs import CausalLMOutputWithPast
+def normalize_gradient_checkpointing_level(value: Union[bool, int, str, None]) -> int:
+    if isinstance(value, bool):
+        return 1 if value else 0
+    if value is None:
+        return 0
+    if isinstance(value, int):
+        return max(0, value)
+    text = str(value).strip().lower()
+    if text in {"", "false", "off", "no", "none"}:
+        return 0
+    if text in {"true", "on", "yes"}:
+        return 1
+    try:
+        return max(0, int(text))
+    except ValueError as exc:
+        raise ValueError(f"Unsupported gradient_checkpointing level: {value!r}") from exc
+class YConfig31(PretrainedConfig):
+    model_type = "ynet31"
+    def __init__(
+        self,
+        dropout: float = 0.0,
+        bos_token_id: int = 151644,
+        eos_token_id: int = 151645,
+        pad_token_id: int = 151643,
+        hidden_act: str = "silu",
+        hidden_size: int = 768,
+        num_hidden_layers: int = 8,
+        max_position_embeddings: int = 8192,
+        vocab_size: int = 6400,
+        rms_norm_eps: float = 1e-6,
+        rope_theta: float = 5e4,
+        rope_scaling: Optional[dict] = None,
+        dtype: str = "float32",
+        self_distill: bool = True,
+        intermediate_size: int = 1536,
+        num_heads: int = 12,
+        mla_kv_lora_rank: int = 64,
+        mla_qk_nope_head_dim: int = 64,
+        mla_qk_rope_head_dim: int = 32,
+        mla_attn_impl: str = "absorb",
+        qkv_lora: bool = False,
+        gradient_checkpointing: Union[bool, int, str] = 0,
+        use_sengram: bool = True,
+        sengram_bucket_size: Optional[int] = 4096,
+        sengram_topk: int = 2,
+        engram_bucket_size: Optional[int] = None,
+        engram_topk: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            **kwargs,
+        )
+        self.dropout = dropout
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.max_position_embeddings = max_position_embeddings
+        self.vocab_size = vocab_size
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.dtype = dtype
+        self.self_distill = self_distill
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.mla_kv_lora_rank = mla_kv_lora_rank
+        self.mla_qk_nope_head_dim = mla_qk_nope_head_dim
+        self.mla_qk_rope_head_dim = mla_qk_rope_head_dim
+        self.mla_attn_impl = mla_attn_impl
+        self.qkv_lora = qkv_lora
+        self.gradient_checkpointing = normalize_gradient_checkpointing_level(gradient_checkpointing)
+        self.use_sengram = bool(use_sengram)
+        if engram_bucket_size is not None:
+            sengram_bucket_size = engram_bucket_size
+        if engram_topk is not None:
+            sengram_topk = engram_topk
+        self.sengram_bucket_size = sengram_bucket_size
+        self.sengram_topk = sengram_topk
+        self.engram_bucket_size = self.sengram_bucket_size
+        self.engram_topk = self.sengram_topk
+    @property
+    def head_dim(self) -> int:
+        return self.mla_qk_nope_head_dim + self.mla_qk_rope_head_dim
+    @property
+    def qk_head_dim(self) -> int:
+        return self.head_dim
+    def scale_lvl(self, lvl: int = 0):
+        if lvl == 0:
+            self.hidden_size = 768
+            self.num_hidden_layers = 12
+            self.num_heads = 8
+            self.mla_kv_lora_rank = 256
+            self.mla_qk_nope_head_dim = 128
+            self.mla_qk_rope_head_dim = 64
+            self.intermediate_size = 2048
+            self.use_sengram = True
+            self.sengram_bucket_size = 8192
+            self.sengram_topk = 8
+        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.use_sengram = True
+        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 = 64
+            self.intermediate_size = 1024
+            self.use_sengram = True
+        else:
+            raise ValueError(f"invalid ymodel31 scale level: {lvl}")
+        return self
+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)
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.eps = eps
+    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)
+class MLGA(nn.Module):
+    """Multihead Latent Gated Attention"""
+    def __init__(self, config: YConfig31, 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).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=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)
+        return out, new_past
+class SwiGLU(nn.Module):
+    def __init__(self, config: YConfig31, 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)
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        up, gate = self.up_proj(x), self.gate_proj(x)
+        up = nn.functional.silu(gate) * up
+        return self.down_proj(up)
+class SengramIndexer(nn.Module):
+    def __init__(self, config: YConfig31):
+        super().__init__()
+        self.hidden_size = int(config.hidden_size)
+        self.bucket_size = int(config.sengram_bucket_size or 4096)
+        self.topk = max(1, min(int(config.sengram_topk), self.bucket_size))
+        self.bucket_proj = nn.Linear(self.hidden_size, self.bucket_size, bias=False)
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        bucket_logits = self.bucket_proj(hidden_states)
+        route_scores = torch.softmax(bucket_logits.float(), dim=-1)
+        topk_ids = torch.topk(route_scores, k=self.topk, dim=-1, sorted=False).indices
+        topk_scores = route_scores.gather(-1, topk_ids)
+        denom = topk_scores.sum(dim=-1, keepdim=True).clamp_min(1e-20)
+        topk_scores = (topk_scores / denom).to(bucket_logits.dtype)
+        return topk_ids, topk_scores
+class SengramPLE(nn.Module):
+    def __init__(self, config: YConfig31):
+        super().__init__()
+        self.hidden_size = int(config.hidden_size)
+        self.embedding = nn.Embedding(int(config.sengram_bucket_size or 4096), self.hidden_size)
+        self.key_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self.memory_norm = RMSNorm(self.hidden_size, config.rms_norm_eps)
+        self.key_norm = RMSNorm(self.hidden_size, config.rms_norm_eps)
+        self.query_norm = RMSNorm(self.hidden_size, config.rms_norm_eps)
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        topk_ids: torch.Tensor,
+        topk_scores: torch.Tensor,
+    ) -> torch.Tensor:
+        topk_embed = F.embedding(topk_ids, self.embedding.weight)
+        return (topk_embed * topk_scores.unsqueeze(-1).to(topk_embed.dtype)).sum(dim=-2)
+class YBlock31(nn.Module):
+    def __init__(self, config: YConfig31, layer_id: int):
+        super().__init__()
+        self.use_sengram = bool(config.use_sengram)
+        self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.sengram_ple = SengramPLE(config) if self.use_sengram else None
+        self.attn = MLGA(config, layer_id)
+        self.ffn = SwiGLU(config)
+        self.se1 = SEBlock(config.hidden_size, act=ACT2FN[config.hidden_act])
+        self.se2 = SEBlock(config.hidden_size, act=ACT2FN[config.hidden_act])
+    def forward(
+        self,
+        x: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        past_key_values: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+        attention_mask: Optional[torch.Tensor] = None,
+        route_ids: Optional[torch.Tensor] = None,
+        route_scores: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        if self.use_sengram and route_ids is not None and route_scores is not None and self.sengram_ple is not None:
+            x = x + self.sengram_ple(x, route_ids, route_scores)
+        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))
+        x = x + self.ffn(x0)
+        return x, past
+class YModel31(nn.Module):
+    def __init__(self, config: YConfig31):
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.num_layers = config.num_hidden_layers
+        self.dropout = config.dropout
+        self.use_sengram = bool(config.use_sengram)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.sengram_indexer = SengramIndexer(config) if self.use_sengram else None
+        self.sengram = self.sengram_indexer
+        self.layers = nn.ModuleList([YBlock31(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 = self.embed_tokens(input_ids)
+        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))
+        route_ids = None
+        route_scores = None
+        if self.use_sengram and self.sengram_indexer is not None:
+            route_ids, route_scores = self.sengram_indexer(x)
+        new_past = [] if use_cache else 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(
+                x,
+                position_embeddings=position_embeddings,
+                past_key_values=past,
+                attention_mask=attention_mask,
+                use_cache=use_cache,
+                route_ids=route_ids,
+                route_scores=route_scores,
+            )
+            if use_cache:
+                new_past.append(layer_past)
+        return self.norm(x), new_past
+class YForCausalLM31(PreTrainedModel, GenerationMixin):
+    config_class = YConfig31
+    def __init__(self, config: Optional[YConfig31] = None):
+        self.config = config or YConfig31()
+        super().__init__(self.config)
+        self.model = YModel31(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 = CausalLMOutputWithPast()
+        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 = 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)
+        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
+        logits_processor = kwargs.get("logits_processor", 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 logits_processor is not None:
+                    logits = logits_processor(input_ids, logits)
+                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)
+                if attention_mask is not None:
+                    attention_mask = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)
+                past_key_values = outputs.past_key_values
+                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
+def count_parameters(config: YConfig31) -> int:
+    return sum(p.numel() for p in YForCausalLM31(config).parameters())
+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_ymodel31_eval(path: Union[str, Path], config: Optional[YConfig31] = None, strict: bool = True) -> YForCausalLM31:
+    path = Path(path)
+    if config is None:
+        config_path = path / "config.json" if path.is_dir() else 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 = YConfig31.from_json_file(str(config_path))
+    model = YForCausalLM31(config)
+    state = _load_state_dict(path)
+    model.load_state_dict(state, strict=strict)
+    model.eval()
+    return model
+YModel31Eval = YModel31
+YForCausalLM31Eval = YForCausalLM31
+__all__ = [
+    "MLGA",
+    "RMSNorm",
+    "SEBlock",
+    "SengramIndexer",
+    "SengramPLE",
+    "SwiGLU",
+    "YBlock31",
+    "YConfig31",
+    "YForCausalLM31",
+    "YForCausalLM31Eval",
+    "YModel31",
+    "YModel31Eval",
+    "apply_rope_to_single",
+    "count_parameters",
+    "load_ymodel31_eval",
+    "precompute_freqs_cis",
+]