YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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"])

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