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---
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 |