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README.md

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+---
+library_name: transformers
+base_model:
+- meituan-longcat/LongCat-Flash-Lite
+---
+
+This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [meituan-longcat/LongCat-Flash-Lite](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite).
+
+| File path | Size |
+|------|------|
+| model.safetensors | 8.4MB |
+
+
+### Example usage:
+
+```python
+import torch
+import transformers
+
+model_id = "tiny-random/longcat-flash-ngram"
+pipe = transformers.pipelines.pipeline(
+    'text-generation',
+    model=model_id,
+    trust_remote_code=True,
+    device_map='cuda',
+    torch_dtype=torch.bfloat16,
+)
+past_key_values = transformers.DynamicCache(config=None)  # set config to None
+r = pipe('Hello, world!', past_key_values=past_key_values, max_new_tokens=32)
+print(r)
+```
+
+### Codes to create this repo:
+
+<details>
+<summary>Python codes</summary>
+
+```python
+import json
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from huggingface_hub import file_exists, hf_hub_download
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoProcessor,
+    AutoTokenizer,
+    GenerationConfig,
+    set_seed,
+)
+from transformers.models.glm4_moe.modeling_glm4_moe import Glm4MoeRMSNorm
+source_model_id = "meituan-longcat/LongCat-Flash-Lite"
+save_folder = "/tmp/tiny-random/longcat-flash-ngram"
+
+Path(save_folder).mkdir(parents=True, exist_ok=True)
+tokenizer = AutoTokenizer.from_pretrained(source_model_id, trust_remote_code=True)
+tokenizer.save_pretrained(save_folder)
+
+with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
+    config_json = json.load(f)
+for k, v in config_json['auto_map'].items():
+    config_json['auto_map'][k] = f'{source_model_id}--{v}'
+config_json.update({
+    'num_layers': 2,
+    'hidden_size': 8,
+    'ffn_hidden_size': 32,
+    'expert_ffn_hidden_size': 32,
+    'num_attention_heads': 4,
+    'kv_lora_rank': 384,
+    'n_routed_experts': 32,
+    'q_lora_rank': 32,
+    'qk_nope_head_dim': 64,
+    'qk_rope_head_dim': 192,
+    'head_dim': 192,
+    'qk_head_dim': 256,
+    'v_head_dim': 64,
+    'moe_topk': 12,
+    'zero_expert_num': 16,
+    'emb_split_num': 2,
+    'emb_neighbor_num': 2,
+    'ngram_vocab_size_ratio': 4,
+})
+# del config_json['quantization_config']
+with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
+    json.dump(config_json, f, indent=2)
+
+config = AutoConfig.from_pretrained(
+    save_folder,
+    trust_remote_code=True,
+)
+print(config)
+torch.set_default_dtype(torch.bfloat16)
+model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
+if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
+    model.generation_config = GenerationConfig.from_pretrained(
+        source_model_id, trust_remote_code=True,
+    )
+model = model.cpu()
+# MTP
+model.model.mtp = nn.ModuleDict({
+    "layers": nn.ModuleList([nn.ModuleDict(dict(
+        eh_proj=nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False),
+        enorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
+        hnorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
+        input_layernorm=nn.RMSNorm(config.hidden_size),
+        post_attention_layernorm=nn.RMSNorm(config.hidden_size),
+        self_attn=deepcopy(model.model.layers[0].self_attn[0]),
+        transformer_layer=nn.ModuleDict({"mlp": deepcopy(model.model.layers[0].mlps[0])}),
+    ))]),
+    "norm": nn.RMSNorm(config.hidden_size),
+})
+for i in range(config.num_layers):
+    model.model.layers[i].mlp.router = model.model.layers[i].mlp.router.float()
+    # model.model.layers[i].mlp.router.e_score_correction_bias = torch.zeros((config.n_routed_experts + config.zero_expert_num)).float()
+set_seed(42)
+with torch.no_grad():
+    for name, p in sorted(model.named_parameters()):
+        torch.nn.init.normal_(p, 0, 0.1)
+        print(name, p.shape, p.dtype)
+model.model.mtp.embed_tokens = deepcopy(model.model.embed_tokens)
+model.model.ngram_embeddings = None  # avoid saving shared params
+
+model.save_pretrained(save_folder)
+torch.set_default_dtype(torch.float32)
+
+with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
+    config_json = json.load(f)
+    config_json['auto_map'] = {k: source_model_id + '--' +
+                               v.split('--')[-1] for k, v in config_json['auto_map'].items()}
+with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
+    json.dump(config_json, f, indent=2)
+for f in Path(save_folder).glob('*.py'):
+    f.unlink()
+```
+
+</details>
+
+### Printing the model:
+
+<details><summary>Click to expand</summary>
+
+```text
+LongcatFlashNgramForCausalLM(
+  (model): LongcatFlashNgramModel(
+    (embed_tokens): Embedding(131072, 8)
+    (layers): ModuleList(
+      (0-1): 2 x LongcatFlashDecoderLayer(
+        (mlp): LongcatFlashMoE(
+          (experts): ModuleList(
+            (0-31): 32 x LongcatFlashMLP(
+              (gate_proj): Linear(in_features=8, out_features=32, bias=False)
+              (up_proj): Linear(in_features=8, out_features=32, bias=False)
+              (down_proj): Linear(in_features=32, out_features=8, bias=False)
+              (act_fn): SiLUActivation()
+            )
+            (32-47): 16 x Identity()
+          )
+          (router): LongcatFlashTopkRouter(
+            (classifier): Linear(in_features=8, out_features=48, bias=False)
+          )
+        )
+        (self_attn): ModuleList(
+          (0-1): 2 x LongcatFlashMLA(
+            (q_a_proj): Linear(in_features=8, out_features=32, bias=False)
+            (q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
+            (q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
+            (kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
+            (kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
+            (kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
+            (o_proj): Linear(in_features=256, out_features=8, bias=False)
+          )
+        )
+        (mlps): ModuleList(
+          (0-1): 2 x LongcatFlashMLP(
+            (gate_proj): Linear(in_features=8, out_features=32, bias=False)
+            (up_proj): Linear(in_features=8, out_features=32, bias=False)
+            (down_proj): Linear(in_features=32, out_features=8, bias=False)
+            (act_fn): SiLUActivation()
+          )
+        )
+        (input_layernorm): ModuleList(
+          (0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
+        )
+        (post_attention_layernorm): ModuleList(
+          (0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
+        )
+      )
+    )
+    (norm): LongcatFlashRMSNorm((8,), eps=1e-05)
+    (rotary_emb): LongcatFlashRotaryEmbedding()
+    (ngram_embeddings): None
+    (mtp): ModuleDict(
+      (layers): ModuleList(
+        (0): ModuleDict(
+          (eh_proj): Linear(in_features=16, out_features=8, bias=False)
+          (enorm): ModuleDict(
+            (m): RMSNorm((8,), eps=None, elementwise_affine=True)
+          )
+          (hnorm): ModuleDict(
+            (m): RMSNorm((8,), eps=None, elementwise_affine=True)
+          )
+          (input_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
+          (post_attention_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
+          (self_attn): LongcatFlashMLA(
+            (q_a_proj): Linear(in_features=8, out_features=32, bias=False)
+            (q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
+            (q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
+            (kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
+            (kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
+            (kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
+            (o_proj): Linear(in_features=256, out_features=8, bias=False)
+          )
+          (transformer_layer): ModuleDict(
+            (mlp): LongcatFlashMLP(
+              (gate_proj): Linear(in_features=8, out_features=32, bias=False)
+              (up_proj): Linear(in_features=8, out_features=32, bias=False)
+              (down_proj): Linear(in_features=32, out_features=8, bias=False)
+              (act_fn): SiLUActivation()
+            )
+          )
+        )
+      )
+      (norm): RMSNorm((8,), eps=None, elementwise_affine=True)
+      (embed_tokens): Embedding(131072, 8)
+    )
+  )
+  (lm_head): Linear(in_features=8, out_features=131072, bias=False)
+)
+```
+
+</details>

chat_template.jinja

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+{%- set tool_choice = tool_choice | default('auto') %}
+{%- set ns = namespace(tool_types = [], last_query_index = -1) %}
+
+{%- if tools and tool_choice != 'none' %}
+    {{- "<longcat_tool_declare>\n"-}}
+    {{- "# Tools\n" }}
+    {{- "You have access to the following tools:\n\n" }}
+    {%- for tool in tools %}
+        {%- if tool.type not in ns.tool_types %}
+            {%- set ns.tool_types = ns.tool_types + [tool.type] %}
+            {{- "## Tool namespace: " ~ tool.type ~ "\n\n" }}
+        {%- endif %}
+        {%- if tool.type == 'code_interpreter' %}
+            {%- set tool = {"type":"code_interpreter","function":{"name":"code_interpreter_preview","description":"The code will be executed in a stateful Jupyter notebook sandbox environment, only supports local computation, data processing, and file operations.\nCode sandbox environment (network isolated) Any external network requests or online API calls are prohibited.\nIf online functionality is needed, please use other permitted tools.\nCode will respond with the output of the execution or time out after 60.0 seconds. ","parameters":{"type":"object","properties":{"language":{"type":"string","description":"The programming language of the code to be executed. Available values: python (Default), java, go, js, ts, c, c++."},"code":{"type":"string","description":"Python code to be executed must not include the following:\n- Importing network libraries such as requests, httplib, etc.\n- Any form of HTTP requests.\n- External API calls.\n- Network port operations. Example: ```python\nimport pandas as pd\npd.DataFrame({'A':[1,2]})\n```"},"timeout":{"type":"number","description":"The maximum execution time of the code, in seconds. Default is 60.0."}}},"required":["code"]}} %}
+        {%- endif %}
+        {{- "### Tool name: " + tool.function.name + "\n" }}
+        {{- "Description: " + tool.function.description + "\n\n" }}
+        {{- "InputSchema: " + tool.function.parameters | tojson(ensure_ascii=False) + "\n\n" }}
+    {%- endfor %}
+    {{- '**Note**: For each function call, output the function name and arguments within the following XML format:\n<longcat_tool_call>{function-name}\n<longcat_arg_key>{arg-key-1}</longcat_arg_key>\n<longcat_arg_value>{arg-value-1}</longcat_arg_value>\n<longcat_arg_key>{arg-key-2}</longcat_arg_key>\n<longcat_arg_value>{arg-value-2}</longcat_arg_value>\n...\n</longcat_tool_call>\n' }}
+    {{- "</longcat_tool_declare>"-}}
+    {%- for idx in range(messages|length - 1) %}
+        {%- set msg = messages[idx] %}
+        {%- if msg.role == 'assistant' and not msg.tool_calls %}
+            {%- set ns.last_query_index = idx %}
+        {%- endif %}
+    {%- endfor%}
+{%- endif %}
+
+{%- for msg in messages %}
+    {%- if msg.role == "system" %}
+        {{- "<longcat_system>" + msg.content }}
+    {%- elif msg.role == "user" %}
+        {{- "<longcat_user>" }}
+        {%- if msg["files"] %}
+            {{- '<longcat_files>\n' ~ msg.files | tojson(indent=2) ~ '\n</longcat_files>' }}
+        {%- endif %}
+        {{- msg.content }}
+    {%- elif msg.role == "assistant" %}
+        {{- "<longcat_assistant>" }}
+        {%- if enable_thinking == true and msg.reasoning_content and ns.tool_types != [] and loop.index0 > ns.last_query_index %}
+            {{- "\n<longcat_think>\n" ~ msg.reasoning_content ~ "\n</longcat_think>\n" }}
+        {%- endif %}
+        {%- if msg.content%}
+            {{- msg.content }}
+        {%- endif %}
+        {%- if msg.tool_calls %}
+            {%- for tool_call in msg.tool_calls -%}
+                {{- "<longcat_tool_call>" ~ tool_call.function.name ~ "\n" -}}
+                {% set _args = tool_call.function.arguments %}
+                {% for k, v in _args.items() %}
+                    {{- "<longcat_arg_key>" ~ k ~ "</longcat_arg_key>\n" -}}
+                    {{- "<longcat_arg_value>" ~ (v if v is string else v | tojson(ensure_ascii=False)) ~ "</longcat_arg_value>\n" -}}
+                {% endfor %}
+                {{- "</longcat_tool_call>\n" }}
+            {%- endfor %}
+        {%- endif %}
+        {{- "</longcat_s>" -}}
+    {%- elif msg.role == "tool" %}
+        {%- if messages[loop.index0 - 1].role != "tool"%}
+            {{- "<longcat_user>" -}}
+        {%- endif %}
+        {{- "<longcat_tool_response>" ~ msg.content ~ "</longcat_tool_response>"-}}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {%- if enable_thinking == true %}
+        {{- " /think_on" }}
+        {%- if thinking_budget %}
+            {%- if thinking_budget < 1024 %}
+                {%- set thinking_budget = 1024 %}
+            {%- endif%}
+            {{- "\nthinking_budget: < " ~ thinking_budget ~ "."}}
+        {%- endif %}
+        {{- " <longcat_assistant><longcat_think>\n"}}
+    {%- elif enable_thinking == false %}
+        {{- " /think_off <longcat_assistant><longcat_think>\n\n</longcat_think>\n" }}
+    {%- else %}
+        {{- "<longcat_assistant>" }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LongcatFlashNgramForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "meituan-longcat/LongCat-Flash-Lite--configuration_longcat_ngram.LongcatFlashNgramConfig",
+    "AutoModel": "meituan-longcat/LongCat-Flash-Lite--modeling_longcat_ngram.LongcatFlashNgramModel",
+    "AutoModelForCausalLM": "meituan-longcat/LongCat-Flash-Lite--modeling_longcat_ngram.LongcatFlashNgramForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "emb_neighbor_num": 2,
+  "emb_split_num": 2,
+  "eos_token_id": 2,
+  "expert_ffn_hidden_size": 32,
+  "ffn_hidden_size": 32,
+  "head_dim": 192,
+  "hidden_act": "silu",
+  "hidden_size": 8,
+  "initializer_range": 0.02,
+  "kv_lora_rank": 384,
+  "max_position_embeddings": 327680,
+  "mla_scale_kv_lora": true,
+  "mla_scale_q_lora": true,
+  "model_type": "longcat_flash_ngram",
+  "moe_topk": 12,
+  "n_routed_experts": 32,
+  "ngram_vocab_size_ratio": 4,
+  "num_attention_heads": 4,
+  "num_hidden_layers": 4,
+  "num_key_value_heads": 4,
+  "num_layers": 2,
+  "q_lora_rank": 32,
+  "qk_head_dim": 256,
+  "qk_nope_head_dim": 64,
+  "qk_rope_head_dim": 192,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "beta_fast": 32.0,
+    "beta_slow": 1.0,
+    "factor": 10.0,
+    "mscale": 1,
+    "mscale_all_dim": 1,
+    "original_max_position_embeddings": 32768,
+    "rope_type": "yarn"
+  },
+  "rope_theta": 5000000.0,
+  "routed_scaling_factor": 6.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "v_head_dim": 64,
+  "vocab_size": 131072,
+  "zero_expert_num": 16,
+  "zero_expert_type": "identity"
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "transformers_version": "4.57.6"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b07278fd0e88b73482a5ad791bdc6f68d086050dae57289ad19f2f9b48539848
+size 8798040

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<longcat_s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</longcat_s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<longcat_pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<longcat_unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,1810 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": true,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<longcat_unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<longcat_s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</longcat_s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<longcat_pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<shift_unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<shift_s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "</shift_s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<shift_pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<mask_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<reponame>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<filename>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<gh_stars>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<issue_closed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<jupyter_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<jupyter_text>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "17": {
+      "content": "<jupyter_code>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "18": {
+      "content": "<jupyter_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "19": {
+      "content": "<empty_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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