Downloads · 30 days
15
58% of all-time downloads
gemma-3-270m-summarize
broadfield-dev/gemma-3-270m-summarize
gemma-3-270m-summarize 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-tuned-0102-0441 on the broadfield-dev/abiseecnndailymailconcise-Broadfield dataset. - Task: CAUSALLM
All-time downloads
26
Public
Parameters
268M
1.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.1 GB · 97%
At a glance
- License
- mit
- Model type
- gemma3_text
- Access
- Public
- Created
- Jan 2, 2026
- Updated
- Jan 2, 2026
- SHA
- 9ddd79ae
Base models
Model card
From the Hugging Face model README
gemma-3-270m-tuned-0102-0441-tuned-0102-1157
This model is a fine-tuned version of broadfield-dev/gemma-3-270m-tuned-0102-0441 on the broadfield-dev/abisee_cnn_dailymail_concise-Broadfield dataset.
Training Details
- Task: CAUSAL_LM
- Epochs: 1
- 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-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))
- Type
- gemma3_text
- License
- mit
- Created
- Jan 2, 2026
- Updated
- Jan 2, 2026