metadata
language:
- en
pipeline_tag: text2text-generation
metrics:
- f1
tags:
- grammatical error correction
- GEC
- english
This is a fine-tuned version of LLAMA2 trained (7b) on spider, sql-create-context.
To initialize the model:
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map=device_map,
trust_remote_code=True
)
Use the tokenizer:
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
To get the prompt:
dataset = dataset.map(
lambda example: {
"input": "### Instruction: \nYou are a powerful text-to-SQL model.
Your job is to answer questions about a database. You are given
a question and context regarding one or more tables. \n\nYou must
output the SQL query that answers the question.
\n\n
### Dialect:\n\nsqlite\n\n
### question:\n\n"+ example["question"]+"
\n\n### Context:\n\n"+example["context"],
"answer": example["answer"]
}
)
To generate text using the model:
output = model.generate(input["input_ids"])