This tiny model is for debugging. It is randomly initialized with the config adapted from moonshotai/Kimi-K2-Instruct.
Example usage:
- vLLM
vllm serve yujiepan/kimi-k2-tiny-random --trust-remote-code
- Transformers
import torch
import transformers
model_id = "yujiepan/kimi-k2-tiny-random"
pipe = transformers.pipelines.pipeline(
'text-generation',
model=model_id,
trust_remote_code=True,
device_map='cuda',
torch_dtype=torch.bfloat16,
)
messages = [
{"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
{"role": "user", "content": [{"type": "text", "text": "Please give a brief self-introduction."}]},
]
print(pipe(messages, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95))
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-K2-Instruct"
save_folder = "/tmp/yujiepan/kimi-k2-tiny-random"
Path(save_folder).mkdir(parents=True, exist_ok=True)
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({
'first_k_dense_replace': 1,
'num_hidden_layers': 2,
'hidden_size': 32,
'intermediate_size': 64,
'kv_lora_rank': 384,
'moe_intermediate_size': 64,
'n_routed_experts': 32,
'n_shared_experts': 1,
'num_attention_heads': 1,
'num_experts_per_tok': 8,
'num_key_value_heads': 1,
'q_lora_rank': 32,
'qk_nope_head_dim': 64,
'qk_rope_head_dim': 192, # vllm mla kernel supports 576 only, FA supports head dim <= 256
'v_head_dim': 64,
'tie_word_embeddings': False,
})
config_json['rope_scaling']['rope_type'] = 'yarn'
del config_json['quantization_config']
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() # cpu is more stable for random initialization across machines
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.2)
print(name, p.shape)
model.save_pretrained(save_folder)
# print(model)
with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
config_json = json.load(f)
config_json['auto_map'] = {k: 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()
with open(f'{save_folder}/modeling_deepseek.py', 'r', encoding='utf-8') as f:
codes = f.read()
codes = codes.replace(
"past_length = past_key_values.seen_tokens",
"past_length = past_key_values.seen_tokens if hasattr(past_key_values, 'seen_tokens') else past_key_values.get_seq_length() # fix cache api deprecation"
)
codes = codes.replace(
"max_cache_length = past_key_values.get_max_length()",
"max_cache_length = past_key_values.get_max_length() if hasattr(past_key_values, 'get_max_length') else past_key_values.get_max_cache_shape() # fix cache api deprecation"
)
with open(f'{save_folder}/modeling_deepseek.py', 'w', encoding='utf-8') as f:
f.write(codes)
Printing the model:
DeepseekV3ForCausalLM(
(model): DeepseekV3Model(
(embed_tokens): Embedding(163840, 32)
(layers): ModuleList(
(0): DeepseekV3DecoderLayer(
(self_attn): DeepseekV3Attention(
(q_a_proj): Linear(in_features=32, out_features=32, bias=False)
(q_a_layernorm): DeepseekV3RMSNorm()
(q_b_proj): Linear(in_features=32, out_features=256, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=32, out_features=576, bias=False)
(kv_a_layernorm): DeepseekV3RMSNorm()
(kv_b_proj): Linear(in_features=384, out_features=128, bias=False)
(o_proj): Linear(in_features=64, out_features=32, bias=False)
(rotary_emb): DeepseekV3YarnRotaryEmbedding()
)
(mlp): DeepseekV3MLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
(input_layernorm): DeepseekV3RMSNorm()
(post_attention_layernorm): DeepseekV3RMSNorm()
)
(1): DeepseekV3DecoderLayer(
(self_attn): DeepseekV3Attention(
(q_a_proj): Linear(in_features=32, out_features=32, bias=False)
(q_a_layernorm): DeepseekV3RMSNorm()
(q_b_proj): Linear(in_features=32, out_features=256, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=32, out_features=576, bias=False)
(kv_a_layernorm): DeepseekV3RMSNorm()
(kv_b_proj): Linear(in_features=384, out_features=128, bias=False)
(o_proj): Linear(in_features=64, out_features=32, bias=False)
(rotary_emb): DeepseekV3YarnRotaryEmbedding()
)
(mlp): DeepseekV3MoE(
(experts): ModuleList(
(0-31): 32 x DeepseekV3MLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
)
(gate): MoEGate()
(shared_experts): DeepseekV3MLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
)
(input_layernorm): DeepseekV3RMSNorm()
(post_attention_layernorm): DeepseekV3RMSNorm()
)
)
(norm): DeepseekV3RMSNorm()
)
(lm_head): Linear(in_features=32, out_features=163840, bias=False)
)