merlinite-sql-7b-thai-instructlab
This model is a fine-tuned version of instructlab/merlinite-7b-lab on an unknown dataset.
Model description
More information needed
How to Use
installing dependencies
!pip install -qU transformers accelerate
To implement the model
from transformers import AutoTokenizer
import transformers
import torch
question = "คะแนนความสามารถทางการเงินสูงสุดสำหรับลูกค้าในแอฟริกาในปี 2022 คือเท่าใด \nHere is a Table: CREATE TABLE financial_capability (id INT, customer_name VARCHAR(50), region VARCHAR(50), score INT, year INT); INSERT INTO financial_capability (id, customer_name, region, score, year) VALUES (1, 'Thabo', 'Africa', 9, 2022), (2, 'Amina', 'Africa', 8, 2022);"
model = "Pavarissy/merlinite-sql-7b-thai-instructlab"
messages = [{"role": "user",
"content": f"{question}"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
# this is model generation part
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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