Update README.md

README.md

@@ -23,6 +23,7 @@ should probably proofread and complete it, then remove this comment. -->
 # mistral-7b-text-to-sql
 
 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the b-mc2/sql-create-context dataset.
+These are the adapter weights, and the code to use these for generation is given below. A full model will be uploaded at a later date.
 
 ## Model description
 
@@ -31,6 +32,64 @@ This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggin
 - License: Apache 2.0
 - Finetuned from model : Mistral-7B-v0.1
 
+## How to get started with the model
+
+```python
+import torch
+from transformers import AutoTokenizer, pipeline
+from datasets import load_dataset
+from peft import AutoPeftModelForCausalLM
+from random import randint
+
+peft_model_id = "delayedkarma/mistral-7b-text-to-sql"
+
+# Load Model with PEFT adapter
+model = AutoPeftModelForCausalLM.from_pretrained(
+  peft_model_id,
+  device_map="auto",
+  torch_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
+# load into pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Load dataset and Convert dataset to OAI messages
+system_message = """You are a 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:
+{schema}"""
+
+def create_conversation(sample):
+  return {
+    "messages": [
+      {"role": "system", "content": system_message.format(schema=sample["context"])},
+      {"role": "user", "content": sample["question"]},
+      {"role": "assistant", "content": sample["answer"]}
+    ]
+  }
+
+# Load dataset from the hub
+dataset = load_dataset("b-mc2/sql-create-context", split="train")
+dataset = dataset.shuffle().select(range(100))
+
+# Convert dataset to OAI messages
+dataset = dataset.map(create_conversation, remove_columns=dataset.features, batched=False)
+
+dataset = dataset.train_test_split(test_size=20/100)
+
+# Evaluate
+eval_dataset = dataset['test']
+rand_idx = randint(0, len(eval_dataset))
+
+# Test on sample
+prompt = pipe.tokenizer.apply_chat_template(eval_dataset[rand_idx]["messages"][:2], tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=False, temperature=0.1, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
+
+print(f"Query:\n{eval_dataset[rand_idx]['messages'][1]['content']}")
+print(f"Original Answer:\n{eval_dataset[rand_idx]['messages'][2]['content']}")
+print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")
+
+```
+
 ## Training procedure
 
 ### Training hyperparameters