--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python base_model: - moonshotai/Kimi-Linear-48B-A3B-Instruct --- This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [moonshotai/Kimi-Linear-48B-A3B-Instruct](https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct). ### Example usage: - vLLM ```bash vllm serve yujiepan/kimi-linear-tiny-random --trust-remote-code ``` - Transformers ```python # tested on transformers==4.57.1 import torch import transformers from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "yujiepan/kimi-linear-tiny-random" model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map="cuda", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) messages = [ {"role": "system", "content": "You are a helpful assistant provided by Moonshot-AI."}, {"role": "user", "content": "Is 123 a prime?"} ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", tokenize=True, ).to(model.device) print(input_ids) generated_ids = model.generate(inputs=input_ids, max_new_tokens=500) response = tokenizer.batch_decode(generated_ids)[0] print(response) ``` ### Codes to create this repo: ```python import json from pathlib import Path import accelerate import torch from huggingface_hub import file_exists, hf_hub_download from transformers import ( AutoConfig, AutoModelForCausalLM, AutoProcessor, AutoTokenizer, GenerationConfig, set_seed, ) source_model_id = "moonshotai/Kimi-Linear-48B-A3B-Instruct" save_folder = "/tmp/yujiepan/kimi-linear-tiny-random" 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='tokenizer_config.json', repo_type='model'), 'r', encoding='utf-8') as f: tokenizer_config_json = json.load(f) tokenizer_config_json['auto_map']['AutoTokenizer'][0] = f'{source_model_id}--' + \ tokenizer_config_json["auto_map"]["AutoTokenizer"][0] with open(f"{save_folder}/tokenizer_config.json", "w", encoding='utf-8') as f: json.dump(tokenizer_config_json, f, indent=2) # hf_hub_download(source_model_id, filename='tiktoken.model', repo_type='model', # local_dir=save_folder, local_dir_use_symlinks=True, cache_dir='/tmp/') 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({ "head_dim": 32, "hidden_size": 8, "intermediate_size": 32, "linear_attn_config": { "full_attn_layers": [4], "head_dim": 32, "kda_layers": [1, 2, 3], "num_heads": 8, "short_conv_kernel_size": 4, }, "num_attention_heads": 8, "num_key_value_heads": 8, "moe_intermediate_size": 32, "num_hidden_layers": 5, }) 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) torch.set_default_dtype(torch.float32) 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, ) set_seed(42) model = model.cpu() n_parms = sum(p.numel() for p in model.parameters()) 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.numel() / n_parms * 100), '%') model.save_pretrained(save_folder) with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f: config_json = json.load(f) config_json['auto_map'] = {k: f'{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 python_file in Path(save_folder).glob('*.py'): python_file.unlink() ``` ### Printing the model: ```text KimiLinearForCausalLM( (model): KimiLinearModel( (embed_tokens): Embedding(163840, 8, padding_idx=163839) (layers): ModuleList( (0): KimiDecoderLayer( (self_attn): KimiDeltaAttention( (q_proj): Linear(in_features=8, out_features=256, bias=False) (k_proj): Linear(in_features=8, out_features=256, bias=False) (v_proj): Linear(in_features=8, out_features=256, bias=False) (q_conv1d): ShortConvolution(256, 256, kernel_size=(4,), stride=(1,), padding=(3,), groups=256, bias=False, activation=silu, backend=triton) (k_conv1d): ShortConvolution(256, 256, kernel_size=(4,), stride=(1,), padding=(3,), groups=256, bias=False, activation=silu, backend=triton) (v_conv1d): ShortConvolution(256, 256, kernel_size=(4,), stride=(1,), padding=(3,), groups=256, bias=False, activation=silu, backend=triton) (f_a_proj): Linear(in_features=8, out_features=32, bias=False) (f_b_proj): Linear(in_features=32, out_features=256, bias=False) (b_proj): Linear(in_features=8, out_features=8, bias=False) (g_a_proj): Linear(in_features=8, out_features=32, bias=False) (g_b_proj): Linear(in_features=32, out_features=256, bias=False) (o_norm): FusedRMSNormGated(32, eps=1e-05, activation=sigmoid) (o_proj): Linear(in_features=256, out_features=8, bias=False) ) (mlp): KimiMLP( (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): KimiRMSNorm() (post_attention_layernorm): KimiRMSNorm() ) (1-2): 2 x KimiDecoderLayer( (self_attn): KimiDeltaAttention( (q_proj): Linear(in_features=8, out_features=256, bias=False) (k_proj): Linear(in_features=8, out_features=256, bias=False) (v_proj): Linear(in_features=8, out_features=256, bias=False) (q_conv1d): ShortConvolution(256, 256, kernel_size=(4,), stride=(1,), padding=(3,), groups=256, bias=False, activation=silu, backend=triton) (k_conv1d): ShortConvolution(256, 256, kernel_size=(4,), stride=(1,), padding=(3,), groups=256, bias=False, activation=silu, backend=triton) (v_conv1d): ShortConvolution(256, 256, kernel_size=(4,), stride=(1,), padding=(3,), groups=256, bias=False, activation=silu, backend=triton) (f_a_proj): Linear(in_features=8, out_features=32, bias=False) (f_b_proj): Linear(in_features=32, out_features=256, bias=False) (b_proj): Linear(in_features=8, out_features=8, bias=False) (g_a_proj): Linear(in_features=8, out_features=32, bias=False) (g_b_proj): Linear(in_features=32, out_features=256, bias=False) (o_norm): FusedRMSNormGated(32, eps=1e-05, activation=sigmoid) (o_proj): Linear(in_features=256, out_features=8, bias=False) ) (block_sparse_moe): KimiSparseMoeBlock( (experts): ModuleList( (0-255): 256 x KimiBlockSparseMLP( (w1): Linear(in_features=8, out_features=32, bias=False) (w2): Linear(in_features=32, out_features=8, bias=False) (w3): Linear(in_features=8, out_features=32, bias=False) (act_fn): SiLUActivation() ) ) (gate): KimiMoEGate() (shared_experts): KimiMLP( (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): KimiRMSNorm() (post_attention_layernorm): KimiRMSNorm() ) (3-4): 2 x KimiDecoderLayer( (self_attn): KimiMLAAttention( (q_proj): Linear(in_features=8, out_features=1536, bias=False) (kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False) (kv_a_layernorm): KimiRMSNorm() (kv_b_proj): Linear(in_features=512, out_features=2048, bias=False) (o_proj): Linear(in_features=1024, out_features=8, bias=False) ) (block_sparse_moe): KimiSparseMoeBlock( (experts): ModuleList( (0-255): 256 x KimiBlockSparseMLP( (w1): Linear(in_features=8, out_features=32, bias=False) (w2): Linear(in_features=32, out_features=8, bias=False) (w3): Linear(in_features=8, out_features=32, bias=False) (act_fn): SiLUActivation() ) ) (gate): KimiMoEGate() (shared_experts): KimiMLP( (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): KimiRMSNorm() (post_attention_layernorm): KimiRMSNorm() ) ) (norm): KimiRMSNorm() ) (lm_head): Linear(in_features=8, out_features=163840, bias=False) ) ```