| | --- |
| | 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 tiny-random/kimi-linear --trust-remote-code |
| | ``` |
| |
|
| | - Transformers |
| |
|
| | ```python |
| | # tested on transformers==4.57.1 |
| | import torch |
| | import transformers |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | model_id = "tiny-random/kimi-linear" |
| | 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/tiny-random/kimi-linear" |
| | |
| | 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) |
| | ) |
| | ``` |
