README.md

---
license: mit
tags:
- causal_lm
- generated_from_trainer
base_model: broadfield-dev/gemma-3-270m-tuned-0105-1022-tuned-0105-1045-tuned-0105-1111-tuned-0105-1133
datasets:
- broadfield-dev/deepmind_narrativeqa-Broadfield
model-index:
- name: gemma-3-270m-tuned-0105-1022-tuned-0105-1045-tuned-0105-1111-tuned-0105-1133-tuned-0105-1735
  results: []
---
# 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](https://huggingface.co/broadfield-dev/gemma-3-270m-tuned-0105-1022-tuned-0105-1045-tuned-0105-1111-tuned-0105-1133) on the [broadfield-dev/deepmind_narrativeqa-Broadfield](https://huggingface.co/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
```python
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))
```