Llama 4 Tiny Random

🚀 Tiny Debugging Model for Transformers

This tiny model is designed for debugging purposes. It is randomly initialized, and its configuration is adapted from meta-llama/Llama-4-Maverick-17B-128E-Instruct.

✨ Features

• Debugging: Specifically built for debugging tasks.
• Random Initialization: Randomly initialized with a customized configuration.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch

from transformers import AutoProcessor, Llama4ForConditionalGeneration

model_id = "yujiepan/llama-4-tiny-random"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
    model_id,
    attn_implementation="sdpa",  # flex attention / flash_attention_2 do not work, debugging...
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": url1},
            {"type": "image", "url": url2},
            {"type": "text", "text": "Can you describe how these two images are similar, and how they differ?"},
        ]
    },
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=32,
)

response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0]
print(response)
print(outputs[0])

Advanced Usage

import json

import torch

from huggingface_hub import hf_hub_download
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoProcessor,
    AutoTokenizer,
    GenerationConfig,
    Llama4ForConditionalGeneration,
    pipeline,
    set_seed,
)

source_model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
save_folder = "/tmp/yujiepan/llama-4-tiny-random"

processor = AutoProcessor.from_pretrained(source_model_id)
processor.save_pretrained(save_folder)

with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r') as f:
    config_json = json.load(f)
config_json["text_config"]["num_hidden_layers"] = 4  # ensure to trigger no-rope & moe
config_json["text_config"]["hidden_size"] = 32
config_json["text_config"]["head_dim"] = 32  # vllm requires dim >= 32
config_json["text_config"]["num_attention_heads"] = 1
config_json["text_config"]["num_key_value_heads"] = 1
config_json['text_config']["use_qk_norm"] = True
config_json['text_config']["attention_chunk_size"] = 128  # llama4 uses chunked attention
config_json["text_config"]["intermediate_size"] = 64
config_json["text_config"]["intermediate_size_mlp"] = 128
config_json["text_config"]["num_local_experts"] = 8
config_json["text_config"]["tie_word_embeddings"] = True

config_json["vision_config"]["num_hidden_layers"] = 2
config_json["vision_config"]["hidden_size"] = 32
config_json["vision_config"]["intermediate_size"] = 128
assert config_json["vision_config"]["intermediate_size"] == int(
    config_json["vision_config"]["hidden_size"] // config_json["vision_config"]["pixel_shuffle_ratio"] ** 2
)
config_json["vision_config"]["num_attention_heads"] = 1
config_json["vision_config"]["projector_input_dim"] = 32
config_json["vision_config"]["projector_output_dim"] = 32
config_json["vision_config"]["vision_output_dim"] = 32
with open(f"{save_folder}/config.json", "w") as f:
    json.dump(config_json, f, indent=2)

config = AutoConfig.from_pretrained(
    save_folder,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = Llama4ForConditionalGeneration(config)
torch.set_default_dtype(torch.float32)
model.generation_config = GenerationConfig.from_pretrained(
    source_model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        torch.nn.init.normal_(p, 0, 0.5)
        print(name, p.shape)
        pass
model.save_pretrained(save_folder)