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This is a unsloth/Phi-3-mini-4k-instruct model, fine-tuned on b-mc2/sql-create-context, Clinton/Text-to-sql-v1 and knowrohit07/know_sql dataset.

Model Usage

Use the unsloth library to laod and run the model.

Install unsloth and other dependencies.

# Installs Unsloth, Xformers (Flash Attention) and all other packages!
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes torch

Use FastLanguageModel to download and laod the model from hf hub.

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "dmedhi/Phi-3-mini-4k-instruct-text2SQL",
    max_seq_length = 2048
    dtype = None
    load_in_4bit = True
)
FastLanguageModel.for_inference(model)

prompt = """Below is a question that describes a SQL function, paired with a table Context that provides SQL table context. Write an answer that fullfils the user query.

### Question:
{}

### Context:
{}

### Answer:
{}"""

inputs = tokenizer(
[
    prompt.format(
        "What is the latest year that has ferrari 166 fl as the winning constructor?",
        """CREATE TABLE table_name_7 (
            year INTEGER,
            winning_constructor VARCHAR
        )""",
        ""
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)
# ["<s> Below is a question that describes a SQL function, paired with a table Context that provides SQL table context. Write an answer that fullfils the user query.\n\n### Question:\nWhat is the latest year that has ferrari 166 fl as the winning constructor?\n\n### Context:\nCREATE TABLE table_name_7 (\n    year INTEGER,\n    winning_constructor VARCHAR\n)\n\n### Answer:\nTo find the latest year that Ferrari 166 FL was the winning constructor, you can use the following SQL query:\n\n```sql\nSELECT MAX(year)\nFROM table_name_7\nWHERE winning_constructor = 'Ferrari 166 FL';\n```\n"]
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