Create README.md

README.md

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+# This is a fine-tuned model, trained on 400+ test scripts, written in Java using `Cucumber` and `Selenium` frameworks.
+Base model used is `Salesforce/Codegen25-7b-multi`. The dataset used can be found at `shyam-incedoinc/qa-finetune-dataset`
+Training metrics can be seen in the metrics section.
+
+# Training Parameters
+```
+    num_train_epochs=25,
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=1,
+    gradient_checkpointing=True,
+    optim="paged_adamw_32bit",
+    #save_steps=save_steps,
+    logging_steps=25,
+    save_strategy="epoch",
+    learning_rate=2e-4,
+    weight_decay=0.001,
+    fp16=True,
+    bf16=False,
+    max_grad_norm=0.3,
+    warmup_ratio=0.03,
+    #max_steps=max_steps,
+    group_by_length=False,
+    lr_scheduler_type="cosine",
+    disable_tqdm=False,
+    report_to="tensorboard",
+    seed=42
+)
+
+LoraConfig(
+        lora_alpha=16,
+        lora_dropout=0.1,
+        r=64,
+        bias="none",
+        task_type="CAUSAL_LM",
+)
+```
+
+# Run the below code block for getting inferences from this model.
+
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+hf_model_repo = "shyam-incedoinc/codellama-7b-hf-peft-qlora-finetuned-qa"
+
+# Get the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(hf_model_repo)
+
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(hf_model_repo, load_in_4bit=True,
+                                             torch_dtype=torch.float16,
+                                             device_map="auto")
+
+# Load dataset from the hub
+hf_data_repo = "shyam-incedoinc/qa-finetune-dataset"
+train_dataset = load_dataset(hf_data_repo, split="train")
+valid_dataset = load_dataset(hf_data_repo, split="validation")
+
+# Load the sample
+sample = valid_dataset[randrange(len(valid_dataset))]['text']
+groundtruth = sample.split("### Output:\n")[1]
+prompt = sample.split("### Output:\n")[0]+"### Output:\n"
+
+# Generate response
+input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
+outputs = model.generate(input_ids=input_ids, max_new_tokens=1024,
+                                do_sample=True, top_p=0.9, temperature=0.6)
+
+# Print the result
+print(f"Generated response:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}")
+print(f"Ground Truth:\n{groundtruth}")
+```