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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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inference: true |
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widget: |
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- text: Hello! |
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example_title: Hello world |
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group: Python |
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base_model: |
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- moonshotai/Kimi-K2-Instruct |
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--- |
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This tiny model is for debugging. It is randomly initialized with the config adapted from [moonshotai/Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct). |
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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 --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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import transformers |
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model_id = "tiny-random/kimi-k2" |
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pipe = transformers.pipelines.pipeline( |
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'text-generation', |
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model=model_id, |
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trust_remote_code=True, |
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device_map='cuda', |
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torch_dtype=torch.bfloat16, |
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) |
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messages = [ |
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{"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."}, |
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{"role": "user", "content": [{"type": "text", "text": "Please give a brief self-introduction."}]}, |
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] |
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print(pipe(messages, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95)) |
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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 |
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from transformers import ( |
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AutoConfig, |
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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-Instruct" |
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save_folder = "/tmp/tiny-random/kimi-k2" |
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Path(save_folder).mkdir(parents=True, exist_ok=True) |
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with open(hf_hub_download(source_model_id, filename='tokenizer_config.json', repo_type='model'), 'r', encoding='utf-8') as f: |
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tokenizer_config_json = json.load(f) |
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tokenizer_config_json['auto_map']['AutoTokenizer'][0] = f'{source_model_id}--' + \ |
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tokenizer_config_json["auto_map"]["AutoTokenizer"][0] |
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with open(f"{save_folder}/tokenizer_config.json", "w", encoding='utf-8') as f: |
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json.dump(tokenizer_config_json, f, indent=2) |
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hf_hub_download(source_model_id, filename='tiktoken.model', repo_type='model', |
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local_dir=save_folder, local_dir_use_symlinks=True, cache_dir='/tmp/') |
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with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f: |
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config_json = json.load(f) |
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for k, v in config_json['auto_map'].items(): |
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config_json['auto_map'][k] = f'{source_model_id}--{v}' |
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config_json.update({ |
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'first_k_dense_replace': 1, |
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'num_hidden_layers': 2, |
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'hidden_size': 32, |
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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, # vllm mla kernel supports 576 only, FA supports head dim <= 256 |
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'v_head_dim': 64, |
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'tie_word_embeddings': False, |
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}) |
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config_json['rope_scaling']['rope_type'] = 'yarn' |
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del config_json['quantization_config'] |
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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 = AutoModelForCausalLM.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() # cpu is more stable for random initialization across machines |
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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, 0.2) |
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print(name, p.shape) |
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model.save_pretrained(save_folder) |
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# print(model) |
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with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f: |
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config_json = json.load(f) |
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config_json['auto_map'] = {k: v.split('--')[-1] for k, v in config_json['auto_map'].items()} |
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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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# for python_file in Path(save_folder).glob('*.py'): |
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# python_file.unlink() |
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with open(f'{save_folder}/modeling_deepseek.py', 'r', encoding='utf-8') as f: |
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codes = f.read() |
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codes = codes.replace( |
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"past_length = past_key_values.seen_tokens", |
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"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" |
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) |
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codes = codes.replace( |
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"max_cache_length = past_key_values.get_max_length()", |
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"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" |
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) |
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with open(f'{save_folder}/modeling_deepseek.py', 'w', encoding='utf-8') as f: |
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f.write(codes) |
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``` |
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### Printing the model: |
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```text |
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DeepseekV3ForCausalLM( |
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(model): DeepseekV3Model( |
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(embed_tokens): Embedding(163840, 32) |
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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=32, 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=32, 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=32, 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=32, out_features=64, bias=False) |
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(up_proj): Linear(in_features=32, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=32, 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=32, 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=32, 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=32, 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=32, out_features=64, bias=False) |
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(up_proj): Linear(in_features=32, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=32, 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=32, out_features=64, bias=False) |
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(up_proj): Linear(in_features=32, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=32, 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=32, out_features=163840, bias=False) |
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) |
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``` |
