README.md

---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-bnb-4bit
pipeline_tag: text-generation
---

### Description

Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone. 

### Context Length
Models are trained on a context length of 8192 tokens.

### How to use

```python
# Prompt
alpaca_prompt = """Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi instruksi dengan tepat.

### Instruksi:
{}

### Masukan:
{}

### Tanggapan:
{}"""

max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

if True:
    from unsloth import FastLanguageModel
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name = "indo-gemma-2b-alpaca",
        max_seq_length = max_seq_length,
        dtype = dtype,
        load_in_4bit = load_in_4bit
    )
    FastLanguageModel.for_inference(model) # Enable native 2x faster inference

inputs = tokenizer(
    [
        alpaca_prompt.format(
            "Sebutkan langkah-langkah membuat nasi goreng!",
            "", # 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 = 256)
```

### Uploaded  model

- **Developed by:** firqaaa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit