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---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
This model is for debugging. It is randomly initialized using the config from [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) but with smaller size.
Codes:
```python
import os
import torch
import transformers
from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
GenerationConfig, pipeline, set_seed)
model_id = "microsoft/Phi-3.5-mini-instruct"
repo_id = "yujiepan/phi-3.5-tiny-random"
save_path = f"/tmp/{repo_id}"
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config.hidden_size = 16
config.intermediate_size = 32
config.num_attention_heads = 4
config.num_hidden_layers = 2
config.num_key_value_heads = 4
config.rope_scaling['long_factor'] = [1.0299, 1.0499]
config.rope_scaling['short_factor'] = [1.05, 1.05]
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)
model = AutoModelForCausalLM.from_config(
config, torch_dtype=torch.bfloat16,
# attn_implementation="sdpa",
trust_remote_code=True,
)
model.generation_config = GenerationConfig.from_pretrained(
model_id, trust_remote_code=True
)
set_seed(42)
with torch.no_grad():
for _, p in sorted(model.named_parameters()):
torch.nn.init.uniform_(p, -0.2, 0.2)
model.save_pretrained(save_path)
pipe = pipeline("text-generation", model=save_path, device="cuda",
trust_remote_code=True, max_new_tokens=20)
print(pipe("Hello World!"))
```
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