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MindWell

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metadata
library_name: peft
license: llama2
language:
  - en
tags:
  - medical
base_model: lmsys/vicuna-7b-v1.5
inference: false

MindWell

MindWell is a chat assistant trained by fine-tuning lmsys/vicuna-7b-v1.5 on two symptom-based depression datasets: BDI-Sen and PsySym. To expedite and optimize the fine-tuning process, we have implemented Low Rank Adaptation (LoRA) techniques, ensuring enhanced efficiency and faster adaptation.

How to Get Started with MindWell

Use the code below to get started with the model.

from peft import PeftModel
from transformers import AutoModelForCausalLM, LlamaTokenizer, GenerationConfig

config = PeftConfig.from_pretrained("irlab-udc/MindWell")
model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-13b-v1.5", device_map="auto")
model = PeftModel.from_pretrained(model, "irlab-udc/MindWell")
tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.5")

def evaluate(input):
  inputs = tokenizer(input, return_tensors="pt")
  input_ids = inputs["input_ids"].cuda()
  generation_output = model.generate(
      input_ids=input_ids,
      generation_config=GenerationConfig(do_sample=True),
      return_dict_in_generate=True,
      output_scores=True,
      max_new_tokens=512,
  )
  for s in generation_output.sequences:
    output = tokenizer.decode(s)
    print("Answer:", output)

evaluate("What can you do?")

Answer: I can analyze the user's comments to determine if they exhibit any signs of depressive symptoms based on the provided list of symptoms. I will justify my decisions by means of excerpts from the user's comments. If I don't know the answer, I will truthfully say that I don't know.

Training

Configurations and Hyperparameters

The following LoraConfig config was used during training:

  • r: 8
  • lora_alpha: 16
  • target_modules: ["q_proj", "v_proj"]
  • lora_dropout: 0.05
  • bias: "none"
  • task_type: "CAUSAL_LM"

The following TrainingArguments config was used during training:

  • per_device_train_batch_size: 64
  • gradient_accumulation_steps: 32
  • warmup_steps: 100
  • num_train_epochs: 20
  • learning_rate: 3e-4
  • fp16=True

The following bitsandbytes quantization config was used during training:

  • quant_method: bitsandbytes
  • load_in_8bit: True
  • load_in_4bit: False
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: fp4
  • bnb_4bit_use_double_quant: False
  • bnb_4bit_compute_dtype: float32

Framework versions

  • PyTorch 2.1.0
  • PEFT 0.5.0
  • 🤗 Transformers 4.34.0
  • 🤗 Datasets 2.14.5
  • 🤗 Tokenizers 0.14.0

Environmental Impact

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

  • Hardware Type: NVIDIA RTX 6000 Ada Generation.
  • Hours used: 20.
  • Cloud Provider: Private infrastructure.
  • Carbon Emitted: 2.59 Kg. CO2 eq.