Phi-3-mini-4k-instruct-bnb-4bit-Clinical-Trail

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
• Merged: ArvindSharma18/Phi-3-mini-4k-instruct-bnb-4bit-Clinical-Trail-Merged

from unsloth import FastLanguageModel
from transformers import TextStreamer
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
        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")

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.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>