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---
language:
- en
library_name: peft
pipeline_tag: text-generation
tags:
- medical
license: cc-by-nc-3.0
---
# MedFalcon v2.1a 40b LoRA - Step 4500
## Model Description
This a model check point release at 4500 steps. For evaluation use only! Limitations:
* LoRA output will be more concise than the base model
* Due to the size, base knowledge may be overwritten from falcon-40b
* Due to the size, more hardware may be required to load falcon-40b when using this LoRA
### Architecture
`nmitchko/medfalconv2-1a-40b-lora'` is a large language model LoRa specifically fine-tuned for medical domain tasks.
It is based on [`Falcon-40b`](https://huggingface.co./tiiuae/falcon-40b) at 40 billion parameters.
The primary goal of this model is to improve question-answering and medical dialogue tasks.
It was trained using [LoRA](https://arxiv.org/abs/2106.09685), specifically [QLora](https://github.com/artidoro/qlora), to reduce memory footprint.
See Training Parameters for more info This Lora supports 4-bit and 8-bit modes.
### Requirements
```
bitsandbytes>=0.39.0
peft
transformers
```
Steps to load this model:
1. Load base model using transformers
2. Apply LoRA using peft
```python
#
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
from peft import PeftModel
model = "tiiuae/falcon-40b"
LoRA = "nmitchko/medfalconv2-1a-40b-lora"
# If you want 8 or 4 bit set the appropriate flags
load_8bit = True
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, LoRA)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"What does the drug ceftrioxone do?\nDoctor:",
max_length=200,
do_sample=True,
top_k=40,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Parameters
The model was trained for 4500 steps or 1 epoch on a custom, unreleased dataset named `medconcat`.
`medconcat` contains only human generated content and weighs in at over 100MiB of raw text.
The below bash script initiated training in `4bit` mode for a rather large LoRA:
| Item | Amount | Units |
|---------------|--------|-------|
| LoRA Rank | 128 | ~ |
| LoRA Alpha | 256 | ~ |
| Learning Rate | 1e-3 | SI |
| Dropout | 5 | % |
```bash
CURRENTDATEONLY=`date +"%b %d %Y"`
sudo nvidia-smi -i 1 -pl 250
export CUDA_VISIBLE_DEVICES=0
nohup python qlora.py \
--model_name_or_path models/tiiuae_falcon-40b \
--output_dir ./loras/medfalcon2.1a-40b \
--logging_steps 100 \
--save_strategy steps \
--data_seed 42 \
--save_steps 200 \
--save_total_limit 40 \
--evaluation_strategy steps \
--eval_dataset_size 1024 \
--max_eval_samples 1000 \
--per_device_eval_batch_size 1 \
--max_new_tokens 32 \
--dataloader_num_workers 3 \
--group_by_length \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--lora_r 128 \
--lora_alpha 256 \
--lora_modules all \
--double_quant \
--quant_type nf4 \
--bf16 \
--bits 4 \
--warmup_ratio 0.03 \
--lr_scheduler_type constant \
--gradient_checkpointing \
--dataset="training/datasets/medconcat/" \
--dataset_format alpaca \
--trust_remote_code=True \
--source_max_len 16 \
--target_max_len 512 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 16 \
--max_steps 4500 \
--eval_steps 1000 \
--learning_rate 0.0001 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.05 \
--weight_decay 0.0 \
--seed 0 > "${CURRENTDATEONLY}-finetune-medfalcon2.1a.log" &
``` |