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README.md
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tags:
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: merlinite-sql-7b-thai-instructlab
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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More information needed
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## Intended uses & limitations
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More information needed
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tags:
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- trl
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- sft
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model-index:
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- name: merlinite-sql-7b-thai-instructlab
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results: []
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language:
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- th
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pipeline_tag: text-generation
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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More information needed
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## How to Use
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installing dependencies
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```bash
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!pip install -qU transformers accelerate
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```
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To implement the model
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```python
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from transformers import AutoTokenizer
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import transformers
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import torch
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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);"
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model = "Pavarissy/merlinite-sql-7b-thai-instructlab"
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messages = [{"role": "user",
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"content": f"{question}"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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# this is model generation part
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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## Intended uses & limitations
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More information needed
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