Create README.md

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- trl
+- sft
+- generated_from_trainer
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+- b-mc2/sql-create-context
+model-index:
+- name: llama3-8b-instruct-text-to-sql
+  results: []
+metrics:
+- accuracy 79.90
+language:
+- en
+---
+
+
+
+# llama3-8b-instruct-text-to-sql
+
+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.
+
+## Model description
+
+More information needed
+
+## 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: 0.0002
+- train_batch_size: 3
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 6
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.0
+- Pytorch 2.2.0+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1
+
+
+### Usage
+
+```python
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "SagarKrishna/Llama_3_8b_Instruct_Text2Sql_FullPrecision_Finetuned"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+
+messages = [
+    {"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)"},
+    {"role": "user", "content": "Which college have both players with position midfielder and players with position defender?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0]
+print(tokenizer.decode(response, skip_special_tokens=True))
+
+#
+#system
+#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.
+#SCHEMA:
+#CREATE TABLE match_season (College VARCHAR, POSITION VARCHAR)
+#user
+#Which college have both players with position midfielder and players with position defender?
+#assistant
+#SELECT College FROM match_season WHERE POSITION = "Midfielder" INTERSECT SELECT College FROM match_season WHERE POSITION = "Defender"
+#
+```