This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from moonshotai/Kimi-Linear-48B-A3B-Instruct.
Example usage:
- vLLM
vllm serve yujiepan/kimi-linear-tiny-random --trust-remote-code
- Transformers
# 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:
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:
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)
)