databricks-facebook-opt2-ft-dolly-UT

01GangaPutraBheeshma/databricks-facebook-opt2-ft-dolly-UT is an open-source language model, a fine-tuned version of facebook/opt-350m, and Supervised Finetuning was used to retrain and finetune the model - a strategy inspired by offline transfer reinforcement learning. This version of Model learn from mixed-quality data without preference labels, delivering exceptional performance. Despite the simple approach, my commitment is to develop a high-performance, commercially viable, open-source large language model, and I continue to make significant strides toward this vision.

Model Details

Model Description

The data on which this model was trained is databricks/databricks-dolly-15k. Within this dataset, you'll discover a compilation of entries featuring a category, an instruction, a context, and a response corresponding to that instruction. The project's objective is to enhance the quality of instructions, inputs, and responses, ensuring they align seamlessly with their designated task category. All textual components should be articulate, providing genuine information. Additionally, responses should strive for completeness while maintaining conciseness.

• Developed by: Uttasarga Singh
• Funded by [optional]: Self
• Shared by [optional]: Self
• Model type: Decoder based Model
• Language(s) (NLP): English
• License: Meta
• Finetuned from model [optional]: facebook/opt-350m

Model Sources [optional]

• Repository: https://github.com/uttasarga9067/dataset-dolly-to-the-rescue
• Paper [optional]: In Development

Uses

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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,
)

Training Data

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Metrics

Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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