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---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- microsoft/Phi-4-mini-flash-reasoning
---
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).
### Example usage:
```python
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:
```python
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:
```text
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)
)
``` |