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--- |
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library_name: transformers |
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base_model: |
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- moonshotai/Kimi-K2.5 |
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--- |
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This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [moonshotai/Kimi-K2.5](https://huggingface.co/moonshotai/Kimi-K2.5). |
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| File path | Size | |
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|------|------| |
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| model.safetensors | 6.19MB | |
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### Example usage: |
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- vLLM |
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```bash |
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vllm serve tiny-random/kimi-k2.5 --trust-remote-code |
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``` |
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- Transformers |
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```python |
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import torch |
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from transformers import AutoModel, AutoProcessor |
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model_id = "tiny-random/kimi-k2.5" |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG" |
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}, |
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{ |
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"type": "text", |
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"text": "describe this image" |
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} |
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], |
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} |
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] |
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processor = AutoProcessor.from_pretrained( |
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model_id, |
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trust_remote_code=True, |
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) |
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model = AutoModel.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="cuda", |
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trust_remote_code=True, |
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) |
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inputs = processor.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_dict=True, |
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return_tensors="pt" |
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).to(model.device) |
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inputs.pop("token_type_ids", None) |
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generated_ids = model.generate(**inputs, max_new_tokens=16) |
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output_text = processor.decode( |
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generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False) |
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print(output_text) |
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``` |
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### Codes to create this repo: |
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```python |
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import json |
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from pathlib import Path |
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import accelerate |
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import torch |
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from huggingface_hub import file_exists, hf_hub_download, list_repo_files |
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from transformers import ( |
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AutoConfig, |
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AutoModel, |
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AutoModelForCausalLM, |
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AutoProcessor, |
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AutoTokenizer, |
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GenerationConfig, |
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set_seed, |
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) |
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source_model_id = "moonshotai/Kimi-K2.5" |
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save_folder = "/tmp/tiny-random/kimi-k25" |
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Path(save_folder).mkdir(parents=True, exist_ok=True) |
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for f in list_repo_files(source_model_id, repo_type="model"): |
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if (f.endswith('.json') or f.endswith('.py') or f.endswith('.model') or f.endswith('.jinja')) and ( |
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not f.endswith('.index.json') |
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): |
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hf_hub_download( |
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repo_id=source_model_id, |
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filename=f, |
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repo_type="model", |
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local_dir=save_folder |
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) |
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def replace_file(filepath, old_string, new_string): |
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with open(filepath, 'r', encoding='utf-8') as f: |
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code = f.read() |
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code = code.replace(old_string, new_string) |
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with open(filepath, 'w', encoding='utf-8') as f: |
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f.write(code) |
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replace_file(f'{save_folder}/configuration_kimi_k25.py', |
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"from configuration_deepseek import DeepseekV3Config", |
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"from transformers import DeepseekV3Config") |
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replace_file(f'{save_folder}/modeling_kimi_k25.py', |
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"use_deterministic_attn=self.use_deterministic_attn", |
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"") |
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with open(f'{save_folder}/config.json') as f: |
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config_json = json.load(f) |
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config_json['text_config'].update({ |
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'first_k_dense_replace': 1, |
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'num_hidden_layers': 2, |
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'hidden_size': 8, |
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'intermediate_size': 64, |
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'kv_lora_rank': 384, |
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'moe_intermediate_size': 64, |
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'n_routed_experts': 32, |
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'n_shared_experts': 1, |
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'num_attention_heads': 1, |
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'num_experts_per_tok': 8, |
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'num_key_value_heads': 1, |
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'q_lora_rank': 32, |
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'qk_nope_head_dim': 64, |
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'qk_rope_head_dim': 192, |
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'v_head_dim': 64, |
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'tie_word_embeddings': False, |
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}) |
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del config_json['text_config']['quantization_config'] |
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config_json['vision_config'].update({ |
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'mm_hidden_size': 64, |
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'text_hidden_size': 8, |
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'vt_hidden_size': 64, |
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'vt_intermediate_size': 128, |
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'vt_num_attention_heads': 2, |
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'vt_num_hidden_layers': 2, |
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}) |
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del config_json['vision_config']['_attn_implementation'] |
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with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: |
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json.dump(config_json, f, indent=2) |
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config = AutoConfig.from_pretrained( |
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save_folder, |
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trust_remote_code=True, |
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) |
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print(config) |
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torch.set_default_dtype(torch.bfloat16) |
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model = AutoModel.from_config(config, trust_remote_code=True) |
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torch.set_default_dtype(torch.float32) |
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if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'): |
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model.generation_config = GenerationConfig.from_pretrained( |
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source_model_id, trust_remote_code=True, |
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) |
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set_seed(42) |
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model = model.cpu() |
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with torch.no_grad(): |
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for name, p in sorted(model.named_parameters()): |
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torch.nn.init.normal_(p, 0, 0.1) |
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print(name, p.shape) |
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model.save_pretrained(save_folder) |
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replace_file(f'{save_folder}/configuration_kimi_k25.py', |
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"from configuration_deepseek import DeepseekV3Config", |
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"from transformers import DeepseekV3Config") |
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replace_file(f'{save_folder}/modeling_kimi_k25.py', |
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"use_deterministic_attn=self.use_deterministic_attn", |
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"") |
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``` |
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### Printing the model: |
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```text |
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KimiK25ForConditionalGeneration( |
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(vision_tower): MoonViT3dPretrainedModel( |
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(patch_embed): MoonVision3dPatchEmbed( |
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(proj): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14)) |
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(pos_emb): Learnable2DInterpPosEmbDivided_fixed() |
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) |
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(encoder): MoonViT3dEncoder( |
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(rope_2d): Rope2DPosEmbRepeated(dim=32, max_height=512, max_width=512, theta_base=10000) |
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(blocks): ModuleList( |
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(0-1): 2 x MoonViTEncoderLayer( |
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(norm0): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(mlp): MLP2( |
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(fc0): Linear(in_features=64, out_features=128, bias=True) |
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(fc1): Linear(in_features=128, out_features=64, bias=True) |
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(activation): PytorchGELUTanh() |
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) |
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(wqkv): Linear(in_features=64, out_features=192, bias=True) |
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(wo): Linear(in_features=64, out_features=64, bias=True) |
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) |
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) |
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(final_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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(mm_projector): PatchMergerMLP( |
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(pre_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(proj): Sequential( |
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(0): Linear(in_features=256, out_features=256, bias=True) |
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(1): GELU(approximate='none') |
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(2): Linear(in_features=256, out_features=8, bias=True) |
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) |
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) |
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(language_model): DeepseekV3ForCausalLM( |
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(model): DeepseekV3Model( |
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(embed_tokens): Embedding(163840, 8, padding_idx=163839) |
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(layers): ModuleList( |
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(0): DeepseekV3DecoderLayer( |
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(self_attn): DeepseekV3Attention( |
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(q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
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(q_a_layernorm): DeepseekV3RMSNorm() |
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(q_b_proj): Linear(in_features=32, out_features=256, bias=False) |
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(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False) |
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(kv_a_layernorm): DeepseekV3RMSNorm() |
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(kv_b_proj): Linear(in_features=384, out_features=128, bias=False) |
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(o_proj): Linear(in_features=64, out_features=8, bias=False) |
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(rotary_emb): DeepseekV3YarnRotaryEmbedding() |
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) |
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(mlp): DeepseekV3MLP( |
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(gate_proj): Linear(in_features=8, out_features=64, bias=False) |
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(up_proj): Linear(in_features=8, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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(input_layernorm): DeepseekV3RMSNorm() |
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(post_attention_layernorm): DeepseekV3RMSNorm() |
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) |
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(1): DeepseekV3DecoderLayer( |
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(self_attn): DeepseekV3Attention( |
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(q_a_proj): Linear(in_features=8, out_features=32, bias=False) |
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(q_a_layernorm): DeepseekV3RMSNorm() |
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(q_b_proj): Linear(in_features=32, out_features=256, bias=False) |
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(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False) |
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(kv_a_layernorm): DeepseekV3RMSNorm() |
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(kv_b_proj): Linear(in_features=384, out_features=128, bias=False) |
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(o_proj): Linear(in_features=64, out_features=8, bias=False) |
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(rotary_emb): DeepseekV3YarnRotaryEmbedding() |
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) |
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(mlp): DeepseekV3MoE( |
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(experts): ModuleList( |
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(0-31): 32 x DeepseekV3MLP( |
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(gate_proj): Linear(in_features=8, out_features=64, bias=False) |
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(up_proj): Linear(in_features=8, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(gate): MoEGate() |
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(shared_experts): DeepseekV3MLP( |
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(gate_proj): Linear(in_features=8, out_features=64, bias=False) |
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(up_proj): Linear(in_features=8, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=8, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(input_layernorm): DeepseekV3RMSNorm() |
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(post_attention_layernorm): DeepseekV3RMSNorm() |
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) |
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) |
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(norm): DeepseekV3RMSNorm() |
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) |
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(lm_head): Linear(in_features=8, out_features=163840, bias=False) |
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) |
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) |
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``` |
