kimi-k2.5-tiny-random

This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from moonshotai/Kimi-K2.5.

Example usage:

• vLLM

vllm serve yujiepan/kimi-k2.5-tiny-random --trust-remote-code

• Transformers

import base64
import requests
import torch
from transformers import AutoModel, AutoProcessor

model_id = "yujiepan/kimi-k2.5-tiny-random"
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:

<details> <summary>Python codes</summary>

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/yujiepan/kimi-k25-tiny-random"

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",
             "")

</details>

Printing the model:

<details><summary>Click to expand</summary>

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)
  )
)

</details>