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gemma-3-270m-context-qa
broadfield-dev/gemma-3-270m-context-qa
gemma-3-270m-context-qa 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-0105-1022-tuned-0105-1045-tuned-0105-1111-tuned-0105-1133 on the broadfield-dev/deepmindnarrativeqa-Broadfield dataset.
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At a glance
- License
- mit
- Model type
- gemma3_text
- Access
- Public
- Created
- Jan 5, 2026
- Updated
- Jan 5, 2026
- SHA
- 3fa06d9f
Base models
Model card
From the Hugging Face model README
broadfield-dev/gemma-3-270m-context-qa
This model is a fine-tuned version of broadfield-dev/gemma-3-270m-tuned-0105-1022-tuned-0105-1045-tuned-0105-1111-tuned-0105-1133 on the broadfield-dev/deepmind_narrativeqa-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-context-qa"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
messages = [
{"role": "system", "content": "Using the context, answer the users question."},
{"role": "user", "content": "Context: {context_content}\n\nQuestion: 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=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Type
- gemma3_text
- License
- mit
- Created
- Jan 5, 2026
- Updated
- Jan 5, 2026