tiny-random/phi-4-flash

This tiny model is for debugging. It is randomly initialized with the config adapted from microsoft/Phi-4-mini-flash-reasoning.

Example usage:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)

model_id = "tiny-random/phi-4-flash"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

messages = [{
    "role": "user",
    "content": "How to solve 3*x^2+4*x+5=1?"
}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)

outputs = model.generate(
    **inputs.to(model.device),
    max_new_tokens=600,
    temperature=0.6,
    top_p=0.95,
    do_sample=True,
)
outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])

print(outputs[0])

Codes to create this repo:

import json
from pathlib import Path

import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoProcessor,
    GenerationConfig,
    set_seed,
)

source_model_id = "microsoft/Phi-4-mini-flash-reasoning"
save_folder = "/tmp/tiny-random/phi-4-flash"

processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)

with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
    config_json = json.load(f)
for key in ['AutoConfig', 'AutoModelForCausalLM']:
    config_json['auto_map'][key] = f'{source_model_id}--' + config_json['auto_map'][key]
automap = config_json['auto_map']
config_json['hidden_size'] = 64
config_json['intermediate_size'] = 64
config_json['num_attention_heads'] = 2
config_json['num_hidden_layers'] = 4
config_json['num_key_value_heads'] = 2
config_json['tie_word_embeddings'] = True
config_json['sliding_window'] = 512
config_json['use_cache'] = True
config_json['mb_per_layer'] = 2  # first layer is mamba

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 = AutoModelForCausalLM.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()  # cpu is more stable for random initialization across machines
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        torch.nn.init.normal_(p, 0, 0.2)
        print(name, p.shape)
model.save_pretrained(save_folder)
print(model)

with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
    config_json = json.load(f)
    config_json['auto_map'] = automap
    config_json['sliding_window'] = 512  # a bugfix for '<' not supported between instances of 'int' and 'list'
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
    json.dump(config_json, f, indent=2)
for python_file in Path(save_folder).glob('*.py'):
    if python_file.name.startswith('modeling_') or python_file.name.startswith('configuration_'):
        python_file.unlink()

Printing the model:

Phi4FlashForCausalLM(
  (model): Phi4FlashModel(
    (embed_tokens): Embedding(200064, 64, padding_idx=199999)
    (embed_dropout): Dropout(p=0.0, inplace=False)
    (layers): ModuleList(
      (0): SambaYDecoderLayer(
        (mlp): SambaYMLP(
          (fc1): Linear(in_features=64, out_features=128, bias=False)
          (fc2): Linear(in_features=64, out_features=64, bias=False)
          (activation_fn): SiLU()
        )
        (input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
        (attn): Phi3Mamba(
          (in_proj): Linear(in_features=64, out_features=256, bias=False)
          (conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128)
          (act): SiLU()
          (x_proj): Linear(in_features=128, out_features=36, bias=False)
          (dt_proj): Linear(in_features=4, out_features=128, bias=True)
          (out_proj): Linear(in_features=128, out_features=64, bias=False)
        )
        (resid_attn_dropout): Dropout(p=0.0, inplace=False)
        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)
        (post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      )
      (1): SambaYDecoderLayer(
        (mlp): SambaYMLP(
          (fc1): Linear(in_features=64, out_features=128, bias=False)
          (fc2): Linear(in_features=64, out_features=64, bias=False)
          (activation_fn): SiLU()
        )
        (input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
        (attn): SambaYFlashAttention2(
          (out_proj): Linear(in_features=64, out_features=64, bias=True)
          (Wqkv): Linear(in_features=64, out_features=192, bias=True)
          (inner_cross_attn): FlashDiffCustomAttention(
            (subln): SambaYRMSNorm()
          )
        )
        (resid_attn_dropout): Dropout(p=0.0, inplace=False)
        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)
        (post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      )
      (2): SambaYDecoderLayer(
        (mlp): SambaYMLP(
          (fc1): Linear(in_features=64, out_features=128, bias=False)
          (fc2): Linear(in_features=64, out_features=64, bias=False)
          (activation_fn): SiLU()
        )
        (input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
        (attn): Phi3Mamba(
          (in_proj): Linear(in_features=64, out_features=256, bias=False)
          (conv1d): Conv1d(128, 128, kernel_size=(4,), stride=(1,), padding=(3,), groups=128)
          (act): SiLU()
          (x_proj): Linear(in_features=128, out_features=36, bias=False)
          (dt_proj): Linear(in_features=4, out_features=128, bias=True)
          (out_proj): Linear(in_features=128, out_features=64, bias=False)
        )
        (resid_attn_dropout): Dropout(p=0.0, inplace=False)
        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)
        (post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      )
      (3): SambaYDecoderLayer(
        (mlp): SambaYMLP(
          (fc1): Linear(in_features=64, out_features=128, bias=False)
          (fc2): Linear(in_features=64, out_features=64, bias=False)
          (activation_fn): SiLU()
        )
        (input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
        (attn): SambaYFlashAttention2(
          (out_proj): Linear(in_features=64, out_features=64, bias=True)
          (Wqkv): Linear(in_features=64, out_features=192, bias=True)
          (inner_cross_attn): FlashDiffCustomAttention(
            (subln): SambaYRMSNorm()
          )
        )
        (resid_attn_dropout): Dropout(p=0.0, inplace=False)
        (resid_mlp_dropout): Dropout(p=0.0, inplace=False)
        (post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
      )
    )
    (final_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=64, out_features=200064, bias=False)
)