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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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- microsoft/Phi-4-mini-flash-reasoning |
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--- |
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This tiny model is for debugging. It is randomly initialized with the config adapted from [microsoft/Phi-4-mini-flash-reasoning](https://huggingface.co/microsoft/Phi-4-mini-flash-reasoning). |
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### Example usage: |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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torch.random.manual_seed(0) |
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model_id = "tiny-random/phi-4-flash" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="cuda", |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
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messages = [{ |
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"role": "user", |
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"content": "How to solve 3*x^2+4*x+5=1?" |
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}] |
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inputs = tokenizer.apply_chat_template( |
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messages, |
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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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) |
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outputs = model.generate( |
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**inputs.to(model.device), |
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max_new_tokens=600, |
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temperature=0.6, |
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top_p=0.95, |
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do_sample=True, |
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) |
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outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:]) |
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print(outputs[0]) |
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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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GenerationConfig, |
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set_seed, |
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) |
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source_model_id = "microsoft/Phi-4-mini-flash-reasoning" |
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save_folder = "/tmp/tiny-random/phi-4-flash" |
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processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True) |
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processor.save_pretrained(save_folder) |
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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 key in ['AutoConfig', 'AutoModelForCausalLM']: |
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config_json['auto_map'][key] = f'{source_model_id}--' + config_json['auto_map'][key] |
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automap = config_json['auto_map'] |
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config_json['hidden_size'] = 64 |
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config_json['intermediate_size'] = 64 |
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config_json['num_attention_heads'] = 2 |
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config_json['num_hidden_layers'] = 4 |
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config_json['num_key_value_heads'] = 2 |
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config_json['tie_word_embeddings'] = True |
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config_json['sliding_window'] = 512 |
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config_json['use_cache'] = True |
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config_json['mb_per_layer'] = 2 # first layer is mamba |
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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'] = automap |
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config_json['sliding_window'] = 512 # a bugfix for '<' not supported between instances of 'int' and 'list' |
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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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if python_file.name.startswith('modeling_') or python_file.name.startswith('configuration_'): |
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python_file.unlink() |
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``` |
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### Printing the model: |
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```text |
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Phi4FlashForCausalLM( |
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(model): Phi4FlashModel( |
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(embed_tokens): Embedding(200064, 64, padding_idx=199999) |
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(embed_dropout): Dropout(p=0.0, inplace=False) |
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(layers): ModuleList( |
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(0): SambaYDecoderLayer( |
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(mlp): SambaYMLP( |
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(fc1): Linear(in_features=64, out_features=128, bias=False) |
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(fc2): Linear(in_features=64, out_features=64, bias=False) |
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(activation_fn): SiLU() |
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) |
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(input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(attn): Phi3Mamba( |
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(in_proj): Linear(in_features=64, out_features=256, bias=False) |
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(conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128) |
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(act): SiLU() |
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(x_proj): Linear(in_features=128, out_features=36, bias=False) |
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(dt_proj): Linear(in_features=4, out_features=128, bias=True) |
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(out_proj): Linear(in_features=128, out_features=64, bias=False) |
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) |
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(resid_attn_dropout): Dropout(p=0.0, inplace=False) |
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(resid_mlp_dropout): Dropout(p=0.0, inplace=False) |
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(post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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) |
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(1): SambaYDecoderLayer( |
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(mlp): SambaYMLP( |
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(fc1): Linear(in_features=64, out_features=128, bias=False) |
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(fc2): Linear(in_features=64, out_features=64, bias=False) |
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(activation_fn): SiLU() |
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) |
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(input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(attn): SambaYFlashAttention2( |
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(out_proj): Linear(in_features=64, out_features=64, bias=True) |
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(Wqkv): Linear(in_features=64, out_features=192, bias=True) |
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(inner_cross_attn): FlashDiffCustomAttention( |
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(subln): SambaYRMSNorm() |
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) |
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) |
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(resid_attn_dropout): Dropout(p=0.0, inplace=False) |
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(resid_mlp_dropout): Dropout(p=0.0, inplace=False) |
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(post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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) |
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(2): SambaYDecoderLayer( |
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(mlp): SambaYMLP( |
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(fc1): Linear(in_features=64, out_features=128, bias=False) |
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(fc2): Linear(in_features=64, out_features=64, bias=False) |
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(activation_fn): SiLU() |
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) |
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(input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(attn): Phi3Mamba( |
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(in_proj): Linear(in_features=64, out_features=256, bias=False) |
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(conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128) |
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(act): SiLU() |
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(x_proj): Linear(in_features=128, out_features=36, bias=False) |
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(dt_proj): Linear(in_features=4, out_features=128, bias=True) |
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(out_proj): Linear(in_features=128, out_features=64, bias=False) |
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) |
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(resid_attn_dropout): Dropout(p=0.0, inplace=False) |
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(resid_mlp_dropout): Dropout(p=0.0, inplace=False) |
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(post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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) |
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(3): SambaYDecoderLayer( |
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(mlp): SambaYMLP( |
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(fc1): Linear(in_features=64, out_features=128, bias=False) |
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(fc2): Linear(in_features=64, out_features=64, bias=False) |
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(activation_fn): SiLU() |
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) |
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(input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(attn): SambaYFlashAttention2( |
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(out_proj): Linear(in_features=64, out_features=64, bias=True) |
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(Wqkv): Linear(in_features=64, out_features=192, bias=True) |
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(inner_cross_attn): FlashDiffCustomAttention( |
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(subln): SambaYRMSNorm() |
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) |
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) |
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(resid_attn_dropout): Dropout(p=0.0, inplace=False) |
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(resid_mlp_dropout): Dropout(p=0.0, inplace=False) |
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(post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=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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(lm_head): Linear(in_features=64, out_features=200064, bias=False) |
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) |
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``` |
