This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from moonshotai/Kimi-K2.5.
| File path | Size |
|---|---|
| model.safetensors | 6.2MB |
Example usage:
- vLLM
vllm serve tiny-random/kimi-k2.5 --trust-remote-code
- Transformers
import base64
import requests
import torch
from transformers import AutoModel, AutoProcessor
model_id = "tiny-random/kimi-k2.5"
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"
image_base64 = base64.b64encode(requests.get(image_url).content).decode()
messages = [
{
'role': 'user',
'content': [
{'type': 'text', 'text': 'Describe this image in detail.'},
{
'type': 'image_url',
'image_url': f'data:image/png;base64,{image_base64}',
},
],
}
]
processor = AutoProcessor.from_pretrained(
model_id,
trust_remote_code=True,
)
model = AutoModel.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="cuda",
trust_remote_code=True,
)
inputs = processor(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=16)
output_text = processor.decode(
generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)
Codes to create this repo:
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download, list_repo_files
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "moonshotai/Kimi-K2.5"
save_folder = "/tmp/tiny-random/kimi-k25"
Path(save_folder).mkdir(parents=True, exist_ok=True)
for f in list_repo_files(source_model_id, repo_type="model"):
if (f.endswith('.json') or f.endswith('.py') or f.endswith('.model') or f.endswith('.jinja')) and (
not f.endswith('.index.json')
):
hf_hub_download(
repo_id=source_model_id,
filename=f,
repo_type="model",
local_dir=save_folder
)
def replace_file(filepath, old_string, new_string):
with open(filepath, 'r', encoding='utf-8') as f:
code = f.read()
code = code.replace(old_string, new_string)
with open(filepath, 'w', encoding='utf-8') as f:
f.write(code)
replace_file(f'{save_folder}/configuration_kimi_k25.py',
"from configuration_deepseek import DeepseekV3Config",
"from transformers import DeepseekV3Config")
replace_file(f'{save_folder}/modeling_kimi_k25.py',
"use_deterministic_attn=self.use_deterministic_attn",
"")
with open(f'{save_folder}/config.json') as f:
config_json = json.load(f)
config_json['text_config'].update({
'first_k_dense_replace': 1,
'num_hidden_layers': 2,
'hidden_size': 8,
'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,
'v_head_dim': 64,
'tie_word_embeddings': False,
})
del config_json['text_config']['quantization_config']
config_json['vision_config'].update({
'mm_hidden_size': 64,
'text_hidden_size': 8,
'vt_hidden_size': 64,
'vt_intermediate_size': 128,
'vt_num_attention_heads': 2,
'vt_num_hidden_layers': 2,
})
del config_json['vision_config']['_attn_implementation']
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 = AutoModel.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()
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
replace_file(f'{save_folder}/configuration_kimi_k25.py',
"from configuration_deepseek import DeepseekV3Config",
"from transformers import DeepseekV3Config")
replace_file(f'{save_folder}/modeling_kimi_k25.py',
"use_deterministic_attn=self.use_deterministic_attn",
"")
Printing the model:
KimiK25ForConditionalGeneration(
(vision_tower): MoonViT3dPretrainedModel(
(patch_embed): MoonVision3dPatchEmbed(
(proj): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14))
(pos_emb): Learnable2DInterpPosEmbDivided_fixed()
)
(encoder): MoonViT3dEncoder(
(rope_2d): Rope2DPosEmbRepeated(dim=32, max_height=512, max_width=512, theta_base=10000)
(blocks): ModuleList(
(0-1): 2 x MoonViTEncoderLayer(
(norm0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(mlp): MLP2(
(fc0): Linear(in_features=64, out_features=128, bias=True)
(fc1): Linear(in_features=128, out_features=64, bias=True)
(activation): PytorchGELUTanh()
)
(wqkv): Linear(in_features=64, out_features=192, bias=True)
(wo): Linear(in_features=64, out_features=64, bias=True)
)
)
(final_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
)
(mm_projector): PatchMergerMLP(
(pre_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(proj): Sequential(
(0): Linear(in_features=256, out_features=256, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=256, out_features=8, bias=True)
)
)
(language_model): DeepseekV3ForCausalLM(
(model): DeepseekV3Model(
(embed_tokens): Embedding(163840, 8, padding_idx=163839)
(layers): ModuleList(
(0): DeepseekV3DecoderLayer(
(self_attn): DeepseekV3Attention(
(q_a_proj): Linear(in_features=8, 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=8, 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=8, bias=False)
(rotary_emb): DeepseekV3YarnRotaryEmbedding()
)
(mlp): DeepseekV3MLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
(input_layernorm): DeepseekV3RMSNorm()
(post_attention_layernorm): DeepseekV3RMSNorm()
)
(1): DeepseekV3DecoderLayer(
(self_attn): DeepseekV3Attention(
(q_a_proj): Linear(in_features=8, 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=8, 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=8, bias=False)
(rotary_emb): DeepseekV3YarnRotaryEmbedding()
)
(mlp): DeepseekV3MoE(
(experts): ModuleList(
(0-31): 32 x DeepseekV3MLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
)
(gate): MoEGate()
(shared_experts): DeepseekV3MLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
)
(input_layernorm): DeepseekV3RMSNorm()
(post_attention_layernorm): DeepseekV3RMSNorm()
)
)
(norm): DeepseekV3RMSNorm()
)
(lm_head): Linear(in_features=8, out_features=163840, bias=False)
)
)