from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "broadfield-dev/gemma-3-270m-tuned-0102-0441-tuned-0102-1157"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
messages = [
    {"role": "system", "content": "Summarize this: "},
    {"role": "user", "content": "Your input here..."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

broadfield-dev/gemma-3-270m-summarize

gemma-3-270m-tuned-0102-0441-tuned-0102-1157

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broadfield-dev/
gemma-3-270m-summarize