---
base_model: Ichsan2895/Merak-7B-v4
license: llama2
datasets:
- allenai/c4
language:
- id
tags:
- gptq
- mistral
- indonesia
inference: false
---

# Merak-7B-v4 GPTQ
<!-- markdownlint-disable MD041 -->

<!-- header start -->
<!-- 200823 -->
<div style="margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/aMm54ZY.jpg" alt="Merak" style="width: 300px; margin:auto">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->

Utilize the [c4/id]("https://huggingface.co/datasets/allenai/c4/blob/main/multilingual/c4-id.tfrecord-00000-of-01024.json.gz") dataset for the quantization process.

[Merak-7B-v4 GPTQ]("https://huggingface.co/daptheHuman/Merak-7B-v4-GPTQ") is GPTQ version of [Ichsan2895/Merak-7B-v4](https://huggingface.co/Ichsan2895/Merak-7B-v4)

## Python code example: inference from this GPTQ model

### Install the necessary packages

Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```

If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:

```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```

### Example Python code

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "daptheHuman/Merak-7B-v4-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```


## Credits
[TheBloke](https://huggingface.co/TheBloke/) for README template.
[asyafiqe](https://huggingface.co/asyafiqe/) for v3-GPTQ inspiration.