---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- fineinstructions/template_instantiator_training
tags:
- datadreamer
- datadreamer-0.46.0
- synthetic
- text-generation
pipeline_tag: text-generation
---
This model will take a instruction template in the format of [FineTemplates](https://huggingface.co/datasets/fineinstructions/finetemplates) and a document and return an instantiated instruction and answer pair.

The output will be a JSON object.

## Simple Usage Example

```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('fineinstructions/template_instantiator', revision=None)
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained('fineinstructions/template_instantiator', revision=None)
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id, return_full_text=False)

# Run inference to instantiate the instruction template and generate an answer
inputs = [json.dumps({
  "instruction_template": "...",
  "document": "..."
}, indent=2)]
prompts = [tokenizer.apply_chat_template([{'role': 'user', 'content': i}], tokenize=False, add_generation_prompt=True) for i in inputs]
generations = pipe(prompts, max_length=131072, truncation=True, temperature=None, top_p=None, do_sample=False)
output = generations[0][0]['generated_text']
print(output)

##### Output:
# {
# ..
# }
# 
```
---
This model was trained with a synthetic dataset with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card and model card can be found [here](datadreamer.json). The training arguments can be found [here](training_args.json).