transformers safetensors llama text-generation conversational autotrain_compatible endpoints_compatible text-generation-inference region:us
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---

# Relation Extraction model for KBQA

<!-- Provide a quick summary of what the model is/does. -->

This is the fine-tuned version of TinyLlama for the Relation Extraction task.

## Model Details
The model is trained on the [Dataset](https://github.com/dki-lab/GrailQA/tree/main/data), which consists of questions and their related information, such as entities and relations. The relationships in the data are annotated from the Freebase dataset.
The relationships in the data are annotated from the Freebase dataset.

#### How to use
You will need the transformers>=4.34
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.


### Direct Use

```python
import os
import torch
#from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)

from transformers import AutoTokenizer, pipeline, logging
model_name_or_path = "dice-research/Ft_TinnyLlama_QA_RE"
model_basename = "model"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
        model_name_or_path, quantization_config=None, device_map="auto"
    )

pipe = pipeline("text-generation", model=model,tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")

def prompt_REQA(question): 
    messages = [
    {"role": "user", "content": question},
    ]
    
    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    #response=pipe(sparql_prompt)
    response = pipe(prompt,max_new_tokens=20, do_sample=True, temperature=0.6, top_k=5, top_p=0.95)[0]['generated_text']
    return response.split('<|assistant|>\n')[1]

prompt_REQA("how many electronic arts games are available for sale in the united states of america?")
```
