MRNH/llama-2-7b-coder

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"])