Create README.md

README.md

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+---
+datasets:
+- b-mc2/sql-create-context
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-2-sql
+- text-generation
+---
+# Model Description
+
+Our Model is fine tuned on Llama-2 7B model on text-2-sql Dataset on alpaca format described by Meta. The dataset is provided by "b-mc2/sql-create-context" present on Huggingface . We have used QLora, Bits&Bytes, Accelerate and Transformers Library to implement PEFT concept. We have fine-tuned this model based on pre-trained llama-2 7B model provided by 'NousResearch/Llama-2-7b-chat-hf'.
+
+# Inference
+```python
+
+!pip install transformers accelerate
+
+from transformers import pipeline
+pipe = pipeline("text-generation", model="ekshat/Llama-2-7b-chat-finetune-for-text2sql")
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
+model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
+
+context = "CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR)"
+question = "List the name, born state and age of the heads of departments ordered by age."
+
+prompt = f"""Below is an context that describes a sql query, paired with an question that provides further information. Write an answer that appropriately completes the request.
+### Context:
+{context}
+### Question:
+{question}
+### Answer:"""
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+result = pipe(prompt)
+  print(result[0]['generated_text'])
+```