---
base_model: instructlab/merlinite-7b-lab
library_name: peft
license: apache-2.0
tags:
- trl
- sft
model-index:
- name: merlinite-sql-7b-thai-instructlab
  results: []
language:
- th
pipeline_tag: text-generation
---

<!-- 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. -->

# merlinite-sql-7b-thai-instructlab

This model is a fine-tuned version of [instructlab/merlinite-7b-lab](https://huggingface.co/instructlab/merlinite-7b-lab) on an unknown dataset.

## Model description

More information needed

## How to Use
installing dependencies
```bash
!pip install -qU transformers accelerate
```

To implement the model
```python
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