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gemma-3-270m-summarize-tuned-0107-1643
broadfield-dev/gemma-3-270m-summarize-tuned-0107-1643
gemma-3-270m-summarize-tuned-0107-1643 is a machine learning model from broadfield-dev. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model is a fine-tuned version of broadfield-dev/gemma-3-270m-summarize on the broadfield-dev/stacked-summariesxsum-2048-in dataset. - Task: CAUSALLM - Epochs: 2 - Learning Rate: 2e-05
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At a glance
- License
- mit
- Model type
- gemma3_text
- Access
- Public
- Created
- Jan 7, 2026
- Updated
- Jan 7, 2026
- SHA
- 22b674a9
Base models
Model card
From the Hugging Face model README
gemma-3-270m-summarize-tuned-0107-1643
This model is a fine-tuned version of broadfield-dev/gemma-3-270m-summarize on the broadfield-dev/stacked-summaries_xsum-2048-in dataset.
Training Details
- Task: CAUSAL_LM
- Epochs: 2
- Learning Rate: 2e-05
- Gradient Accumulation Steps: 4
Entity Labels
['LABEL_0', 'LABEL_1']
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "broadfield-dev/gemma-3-270m-summarize-tuned-0107-1643"
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))
- Type
- gemma3_text
- License
- mit
- Created
- Jan 7, 2026
- Updated
- Jan 7, 2026