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---
language:
- en
library_name: peft
pipeline_tag: text-generation
tags:
- medical
license: cc-by-nc-3.0
---
# MedFalcon 40b LoRA
## Model Description
### Architecture
`nmitchko/medfalcon-40b-lora` is a large language model LoRa specifically fine-tuned for medical domain tasks.
It is based on [`Falcon-40b-instruct`](https://huggingface.co/tiiuae/falcon-40b-instruct/) 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.
> 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 QLORA
2. Apply LoRA using peft
```python
#
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b-instruct"
LoRA = "nmitchko/medfalcon-40b-lora"
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']}")
``` |