Note
- This is an Experiment to generate Clinical Report Information given some of the attributes which I have trained on. Will be making a better one Soon! Stay Updated
- LORA: ArvindSharma18/Phi-3-mini-4k-instruct-bnb-4bit-Clinical-Trail
How to Use
Note: EOS issue. Will keep on Generating Secondary Outcomes.
from unsloth import FastLanguageModel
import torch
max_seq_length = 4096
dtype = torch.float16
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "ArvindSharma18/Phi-3-mini-4k-instruct-bnb-4bit-Clinical-Trail-Merged",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit
)
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
"Official Title: Randomized Trial of Usual Care vs. Specialized, Phase-specific Care for Youth at Risk for Psychosis"
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs.input_ids, attention_mask = inputs.attention_mask,
streamer = text_streamer, max_new_tokens = 2048, pad_token_id = tokenizer.eos_token_id)
Uploaded model
- Developed by: ArvindSharma18
- Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This model was trained 2x faster with Unsloth and Huggingface's TRL library.
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