| --- |
| license: apache-2.0 |
| library_name: peft |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| - peft |
| base_model: mistralai/Mistral-7B-v0.1 |
| datasets: |
| - b-mc2/sql-create-context |
| model-index: |
| - name: mistral-7b-text-to-sql |
| results: [] |
| reference: |
| - https://www.philschmid.de/fine-tune-llms-in-2024-with-trl |
| language: |
| - en |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # mistral-7b-text-to-sql |
|
|
| - This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the b-mc2/sql-create-context dataset. |
| - These are the adapter weights, and the code to use these for generation is given below. |
| - A full model will be uploaded at a later date. |
| - Primary reference: https://www.philschmid.de/fine-tune-llms-in-2024-with-trl |
|
|
| ## Model description |
|
|
| - Model type: Language model |
| - Language(s) (NLP): English |
| - License: Apache 2.0 |
| - Finetuned from model : Mistral-7B-v0.1 |
|
|
| ## How to get started with the model |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, pipeline |
| from datasets import load_dataset |
| from peft import AutoPeftModelForCausalLM |
| from random import randint |
| |
| peft_model_id = "delayedkarma/mistral-7b-text-to-sql" |
| |
| # Load Model with PEFT adapter |
| model = AutoPeftModelForCausalLM.from_pretrained( |
| peft_model_id, |
| device_map="auto", |
| torch_dtype=torch.float16 |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(peft_model_id) |
| # load into pipeline |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
| |
| # Load dataset and Convert dataset to OAI messages |
| system_message = """You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA. |
| SCHEMA: |
| {schema}""" |
| |
| def create_conversation(sample): |
| return { |
| "messages": [ |
| {"role": "system", "content": system_message.format(schema=sample["context"])}, |
| {"role": "user", "content": sample["question"]}, |
| {"role": "assistant", "content": sample["answer"]} |
| ] |
| } |
| |
| # Load dataset from the hub |
| dataset = load_dataset("b-mc2/sql-create-context", split="train") |
| dataset = dataset.shuffle().select(range(100)) |
| |
| # Convert dataset to OAI messages |
| dataset = dataset.map(create_conversation, remove_columns=dataset.features, batched=False) |
| |
| dataset = dataset.train_test_split(test_size=20/100) |
| |
| # Evaluate |
| eval_dataset = dataset['test'] |
| rand_idx = randint(0, len(eval_dataset)) |
| |
| # Test on sample |
| prompt = pipe.tokenizer.apply_chat_template(eval_dataset[rand_idx]["messages"][:2], tokenize=False, add_generation_prompt=True) |
| outputs = pipe(prompt, max_new_tokens=256, do_sample=False, temperature=0.1, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id) |
| |
| print(f"Query:\n{eval_dataset[rand_idx]['messages'][1]['content']}") |
| print(f"Original Answer:\n{eval_dataset[rand_idx]['messages'][2]['content']}") |
| print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}") |
| |
| ``` |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0002 |
| - train_batch_size: 3 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 6 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: constant |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 3 |
|
|
| ### Framework versions |
|
|
| - PEFT 0.7.2.dev0 |
| - Transformers 4.36.2 |
| - Pytorch 2.2.2 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.2 |
