metadata
license: apache-2.0
library_name: transformers
base_model: mistralai/Mistral-7B-v0.1
datasets:
- b-mc2/sql-create-context
model-index:
- name: mistral-7b-text-to-sql_full-model
results: []
reference:
- https://www.philschmid.de/fine-tune-llms-in-2024-with-trl
language:
- en
pipeline_tag: text2text-generation
mistral-7b-text-to-sql_full-model
- This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the b-mc2/sql-create-context dataset.
- These are the full model weights (merged with adapter weights), and the code to use these for generation is given below.
- 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
import torch
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load model directly
tokenizer = AutoTokenizer.from_pretrained("delayedkarma/mistral-7b-text-to-sql_full-model")
model = AutoModelForCausalLM.from_pretrained("delayedkarma/mistral-7b-text-to-sql_full-model")
text = "How many matched scored 3–6, 7–6(5), 6–3?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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