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