Update README.md

README.md

@@ -3,7 +3,106 @@ datasets:
 - ekshat/text-2-sql-with-context
 language:
 - en
-metrics:
-- accuracy
-pipeline_tag: translation
----
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-2-sql
+- text-generation
+- text2sql
+---
+# Introduction
+Our Model is fine-tuned on Llama-2 7B model on Text-2-SQL Dataset based on Alpaca format described by Stanford. We have used QLora, Bits&Bytes, Accelerate and Transformers Library to implement PEFT concept.
+For more information, please visit [github.com/akshayhedaoo1](https://github.com/akshayhedaoo1/Llama-2-7b-chat-finetune-for-text2sql/tree/Data-Science)
+
+
+# Inference
+```python
+!pip install transformers accelerate xformers bitsandbytes
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql")
+
+# Loading model in 4 bit precision
+model = AutoModelForCausalLM.from_pretrained("ekshat/Llama-2-7b-chat-finetune-for-text2sql", load_in_4bit=True)
+
+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'])
+```
+
+
+# Model Information
+- **model_name = "NousResearch/Llama-2-7b-chat-hf"**
+
+- **dataset_name = "ekshat/text-2-sql-with-context"**
+
+
+# QLoRA parameters
+- **lora_r = 64**
+
+- **lora_alpha = 16**
+
+- **lora_dropout = 0.1**
+
+
+# BitsAndBytes parameters
+- **use_4bit = True**
+
+- **bnb_4bit_compute_dtype = "float16"**
+
+- **bnb_4bit_quant_type = "nf4"**
+
+- **use_nested_quant = False**
+
+
+# Training Arguments parameters
+- **num_train_epochs = 1**
+
+- **fp16 = False**
+
+- **bf16 = False**
+
+- **per_device_train_batch_size = 8**
+
+- **per_device_eval_batch_size = 4**
+
+- **gradient_accumulation_steps = 1**
+
+- **gradient_checkpointing = True**
+
+- **max_grad_norm = 0.3**
+
+- **learning_rate = 2e-4**
+
+- **weight_decay = 0.001**
+
+- **optim = "paged_adamw_32bit"**
+
+- **lr_scheduler_type = "cosine"**
+
+- **max_steps = -1**
+
+- **warmup_ratio = 0.03**
+
+- **group_by_length = True**
+
+- **save_steps = 0**
+
+- **logging_steps = 25**
+
+
+# SFT parameters
+- **max_seq_length = None**
+
+- **packing = False**