---
datasets:
- yahma/alpaca-cleaned
---
# Platypus2-70B-instruct-4bit-gptq

Platypus2-70B-instruct-4bit-gptq is a qunatnized version of [`garage-bAInd/Platypus2-70B-instruct`](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct) using GPTQ Quantnization.
This model is only 35 GB in size in comparision with the original garage-bAInd/Platypus2-70B-instruct 127 GB and can run on a single A6000 GPU

### Benchmark Metrics

will report soon 

### Model Details

* **Quantnized by**:  Mohamad.Alhajar@wiro.ai ;
* **Model type:**   quantnized version of Platypus2-70B-instruct using 4bit quantnization
* **Language(s)**: English
* **License**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))

### Prompt Template
```
### Instruction:

<prompt> (without the <>)

### Response:
```

### Training Dataset

`Platypus2-70B-instruct-4bit-gptq` quantnized using gptq on Alpaca dataset [`yahma/alpaca-cleaned`](https://huggingface.co/datasets/yahma/alpaca-cleaned).

### Training Procedure

`garage-bAInd/Platypus2-70B` was instruction fine-tuned using gptq on 2 L40 48GB. 

## How to Get Started with the Model
First install auto_gptq with 
```shell
pip install auto_gptq
```

Use the code sample provided in the original post to interact with the model.
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
 
model_id = "malhajar/Platypus2-70B-instruct-4bit-gptq"
model = AutoGPTQForCausalLM.from_quantized(model_id,inject_fused_attention=False,
        use_safetensors=True,
        trust_remote_code=False,
        use_triton=False,
        quantize_config=None)

tokenizer = AutoTokenizer.from_pretrained(model_id)

question: "Who was the first person to walk on the moon?"
# For generating a response
prompt = '''
### Instruction:
{question} 

### Response:'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids)
response = tokenizer.decode(output[0])

print(response)
```

### Citations
```bibtex
@article{platypus2023,
    title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, 
    author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
    booktitle={arXiv preprint arxiv:2308.07317},
    year={2023}
}
```
```bibtex
@misc{touvron2023llama,
    title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
    author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov       year={2023},
    eprint={2307.09288},
    archivePrefix={arXiv},
}
```
```bibtex
@misc{frantar2023gptq,
      title={GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers}, 
      author={Elias Frantar and Saleh Ashkboos and Torsten Hoefler and Dan Alistarh},
      year={2023},
      eprint={2210.17323},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```