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