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