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--- |
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base_model: instructlab/merlinite-7b-lab |
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library_name: peft |
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license: apache-2.0 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# merlinite-sql-7b-thai-instructlab |
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This model is a fine-tuned version of [instructlab/merlinite-7b-lab](https://huggingface.co/instructlab/merlinite-7b-lab) on an unknown dataset. |
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## Model description |
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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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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
