Update README.md

README.md

@@ -36,92 +36,172 @@ The data on which this model was trained is databricks/databricks-dolly-15k. Wit
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+### How to get started with this Model
+```
+import torch
+from peft import PeftModel, PeftConfig
+
+model_name = "01GangaPutraBheeshma/facebook_opt2"
+trained_model = AutoModelForCausalLM.from_pretrained(model_name)
+trained_tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = """ Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+if one gets corona and you are self-isolating and it is not severe, is there any meds that one can take?
+
+### Response: """
+input_ids = trained_tokenizer(prompt, return_tensors="pt", truncation=True).input_ids
+
+print(f"After Training Response :")
+outputs = trained_model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=1.0)
+print(f"-------------------------\n\n")
+print(f"Generated instruction:\n{trained_tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")
+print(f"-------------------------\n\n")
+```
+
+### Fine-tuning this Model on your own Dataset(Preprocessing the Input Data)
+
+If you would like to fine-tune this model for other datasets, please try to develop a function, that can make our datasets to be in the same format as our function desires, thus using this below script.
+```
+INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
+INSTRUCTION_KEY = "### Instruction:"
+INPUT_KEY = "Input:"
+RESPONSE_KEY = "### Response:"
+END_KEY = "### End"
+
+PROMPT_NO_INPUT_FORMAT = """{intro}
+
+{instruction_key}
+{instruction}
+
+{response_key}
+{response}
+
+{end_key}""".format(
+  intro=INTRO_BLURB,
+  instruction_key=INSTRUCTION_KEY,
+  instruction="{instruction}",
+  response_key=RESPONSE_KEY,
+  response="{response}",
+  end_key=END_KEY
+)
+
+PROMPT_WITH_INPUT_FORMAT = """{intro}
+
+{instruction_key}
+{instruction}
+
+{input_key}
+{input}
+
+{response_key}
+{response}
+
+{end_key}""".format(
+  intro=INTRO_BLURB,
+  instruction_key=INSTRUCTION_KEY,
+  instruction="{instruction}",
+  input_key=INPUT_KEY,
+  input="{input}",
+  response_key=RESPONSE_KEY,
+  response="{response}",
+  end_key=END_KEY
+)
+
+def apply_prompt_template(examples):
+  instruction = examples["instruction"]
+  response = examples["response"]
+  context = examples.get("context")
+
+  if context:
+    full_prompt = PROMPT_WITH_INPUT_FORMAT.format(instruction=instruction, response=response, input=context)
+  else:
+    full_prompt = PROMPT_NO_INPUT_FORMAT.format(instruction=instruction, response=response)
+  return { "text": full_prompt }
+
+dataset = dataset.map(apply_prompt_template)
+```
+## Training Details and Procedure
+```
+from transformers import TrainingArguments
+from trl import SFTTrainer
+
+output_dir = "./facebook_opt2"
+per_device_train_batch_size = 4
+gradient_accumulation_steps = 4
+optim = "paged_adamw_32bit"
+save_steps = 500
+logging_steps = 100
+learning_rate = 2e-4
+max_grad_norm = 0.3
+max_steps = 1000
+warmup_ratio = 0.03
+lr_scheduler_type = "constant"
+
+training_arguments = TrainingArguments(
+    output_dir=output_dir,
+    per_device_train_batch_size=per_device_train_batch_size,
+    gradient_accumulation_steps=gradient_accumulation_steps,
+    optim=optim,
+    save_steps=save_steps,
+    logging_steps=logging_steps,
+    learning_rate=learning_rate,
+    fp16=True,
+    max_grad_norm=max_grad_norm,
+    max_steps=max_steps,
+    warmup_ratio=warmup_ratio,
+    group_by_length=True,
+    lr_scheduler_type=lr_scheduler_type,
+    ddp_find_unused_parameters=False,
+    push_to_hub=True
+)
+
+max_seq_length = 512
+
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    peft_config=peft_config,
+    dataset_text_field="text",
+    max_seq_length=max_seq_length,
+    tokenizer=tokenizer,
+    args=training_arguments,
+)
+```
+
+output_dir: Directory to save the trained model and logs.
+per_device_train_batch_size: Number of training samples per GPU.
+gradient_accumulation_steps: Number of steps to accumulate gradients before updating the model.
+optim: Optimizer for training (e.g., "paged_adamw_32bit").
+save_steps: Save model checkpoints every N steps.
+logging_steps: Log training information every N steps.
+learning_rate: Initial learning rate for training.
+max_grad_norm: Maximum gradient norm for gradient clipping.
+max_steps: Maximum number of training steps.
+warmup_ratio: Ratio of warmup steps during learning rate warmup.
+lr_scheduler_type: Type of learning rate scheduler (e.g., "constant").
+fp16: Enable mixed-precision training.
+group_by_length: Group training samples by length for efficiency.
+ddp_find_unused_parameters: Enable distributed training parameter setting.
+push_to_hub: Push the trained model to the Hugging Face Model Hub.
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
 [More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Step	Training Loss
+100	      2.189900
+200	      2.014100
+300	      1.957200
+400	      1.990000
+500	      1.985200
+600	      1.986500
+700	      1.964300
+800	      1.951900
+900	      1.936900
+1000	  2.011200
 
 ### Results