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--- |
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license: apache-2.0 |
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library_name: peft |
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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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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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datasets: |
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- b-mc2/sql-create-context |
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model-index: |
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- name: llama3-8b-instruct-text-to-sql |
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results: [] |
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metrics: |
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- accuracy: 79.90% |
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language: |
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- en |
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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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# llama3-8b-instruct-text-to-sql |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. |
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## Model description |
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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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## 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: 0.0002 |
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- train_batch_size: 3 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 6 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 3 |
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### Training results |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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### Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_id = "tyfeng1997/llama3-8b-instruct-text-to-sql" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "system", "content": "You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.\nSCHEMA:\nCREATE TABLE match_season (College VARCHAR, POSITION VARCHAR)"}, |
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{"role": "user", "content": "Which college have both players with position midfielder and players with position defender?"}, |
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] |
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input_ids = tokenizer.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to(model.device) |
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terminators = [ |
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tokenizer.eos_token_id, |
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tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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outputs = model.generate( |
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input_ids, |
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max_new_tokens=256, |
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eos_token_id=terminators, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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) |
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response = outputs[0] |
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print(tokenizer.decode(response, skip_special_tokens=True)) |
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# |
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#system |
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#You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA. |
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#SCHEMA: |
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#CREATE TABLE match_season (College VARCHAR, POSITION VARCHAR) |
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#user |
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#Which college have both players with position midfielder and players with position defender? |
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#assistant |
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#SELECT College FROM match_season WHERE POSITION = "Midfielder" INTERSECT SELECT College FROM match_season WHERE POSITION = "Defender" |
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# |
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``` |