gemma-alpacha

yahma/alpaca-cleaned finetuned with gemma-7b-bnb-4bit

Usage

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("gnumanth/gemma-unsloth-alpaca")

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
    alpaca_prompt.format(
        "Give me a python code for quicksort", # instruction
        "1,-1,0,8,9,-2,2", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)

<bos>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Give me a python code for quicksort

### Input:
1,-1,0,8,9,-2,2

### Response:
def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    left = [i for i in arr[1:] if i < pivot]
    right = [i for i in arr[1:] if i >= pivot]
    return quicksort(left) + [pivot] + quicksort(right)<eos>

Hemanth HMM | (Built with unsloth)