from transformers import AutoTokenizer, AutoModelForImageTextToText
import torch

model_name = "tonyzhao123/dummy_llama4"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForImageTextToText.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example usage
text = "Hello, how are you today?"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.7,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Information

Base Model: meta-llama/Llama-4-Scout-17B-16E
Model Type: llama4
Architecture: Llama4ForConditionalGeneration
Training Step: 12136
Source Checkpoint: checkpoint-12136

This checkpoint was extracted from training step 12136. The model was trained using custom scripts with on-the-fly tokenization on WikiText-103 dataset.

Files Included

config.json - Model configuration
model.safetensors - Model weights (single file, no sharding)
tokenizer.json - Fast tokenizer
tokenizer_config.json - Tokenizer configuration
special_tokens_map.json - Special tokens mapping
generation_config.json - Generation parameters (if available)
chat_template.jinja - Chat template (if available)

Limitations

This is an intermediate checkpoint and may not represent the final trained model
Performance may vary depending on the specific training step
Always evaluate the model on your specific use case

Citation

bibtex

@misc{tonyzhao123_dummy_llama4_checkpoint_12136,
  title={tonyzhao123/dummy_llama4 - Checkpoint 12136},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/tonyzhao123/dummy_llama4}
}