---
inference: false
language:
- en
license: llama2
model_type: llama
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---

# 🦚Merak-7B-v3-Mini-Orca GPTQ🐳
<p align="center">
<img src="https://i.imgur.com/39sQd3h.png" alt="Merak Orca" width="300" height="300"/>
</p>

These files are GPTQ model files for [**Merak-7B-v3-Mini-Orca**](https://huggingface.co/asyafiqe/Merak-7B-v3-Mini-Orca-Indo)

[**Merak-7B-v3-Mini-Orca**](https://huggingface.co/asyafiqe/Merak-7B-v3-Mini-Orca-Indo) is Ichsan2895's [Merak-7B-v3](https://huggingface.co/Ichsan2895/Merak-7B-v3) fine-tuned on Bahasa Indonesia translated psmathur's [orca_mini_v1_dataset](https://huggingface.co/datasets/psmathur/orca_mini_v1_dataset).


### Prompt format
You can use [Vicuna 1.1](https://github.com/oobabooga/text-generation-webui/blob/main/instruction-templates/Vicuna-v1.1.yaml) format for Ooobabooga's text generation webui.
```
SYSTEM: Anda adalah asisten AI. Anda akan diberi tugas. Anda harus menghasilkan jawaban yang rinci dan panjang.
USER: <prompt> (without the <>)
ASSISTANT:
```

## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `asyafiqe/Merak-7B-v3-Mini-Orca-Indo-GPTQ`.
  - To download from a specific branch, enter for example `asyafiqe/Merak-7B-v3-Mini-Orca-Indo-GPTQ`
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done"
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Merak-7B-v3-Mini-Orca-Indo-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
  * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!

## How to use this GPTQ model from Python code

First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:

`GITHUB_ACTIONS=true pip install auto-gptq`

Then try the following example code:

```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

model_name_or_path = "asyafiqe/Merak-7B-v3-Mini-Orca-Indo-GPTQ"
model_basename = "model"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=True,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)


prompt = "Tell me about AI"
system_message = "Anda adalah asisten AI. Anda akan diberi tugas. Anda harus menghasilkan jawaban yang rinci dan panjang.\n"
prompt_template=f'''SYSTEM: {system_message}
USER: {prompt}
ASSISTANT: '''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])
```

## Compatibility

The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.

ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

## Credits
[TheBloke](https://huggingface.co/TheBloke/) for the Readme template.