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---
library_name: peft
base_model: TheBloke/Llama-2-7b-Chat-GPTQ
pipeline_tag: text-generation
inference: false
license: openrail
language:
- en
datasets:
- flytech/python-codes-25k
tags:
- text2code
- LoRA
- GPTQ
- Llama-2-7B-Chat
- text2python
- instruction2code
---
# Llama-2-7b-Chat-GPTQ fine-tuned on PYTHON-CODES-25K
Generate Python code that accomplishes the task instructed.
## LoRA Adpater Head
### Description
Parameter Efficient Finetuning(PEFT) a 4bit quantized Llama-2-7b-Chat from TheBloke/Llama-2-7b-Chat-GPTQ on flytech/python-codes-25k dataset.
- **Language(s) (NLP):** English
- **License:** openrail
- **Qunatization:** GPTQ 4bit
- **PEFT:** LoRA
- **Finetuned from model [TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ)**
- **Dataset:** [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k)
## Intended uses & limitations
Addressing the efficay of Quantization and PEFT. Implemented as a personal Project.
### How to use
```
The quantized model is finetuned as PEFT. We have the trained Adapter.
Merging LoRA adapated with GPTQ quantized model is not yet supported.
So instead of loading a single finetuned model, we need to load the mase model and merge the finetuned adapter on top.
```
```python
instruction = """model_input = "Help me set up my daily to-do list!""""
```
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
config = PeftConfig.from_pretrained("SwastikM/Llama-2-7B-Chat-text2code")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7b-Chat-GPTQ")
model = PeftModel.from_pretrained(model, "SwastikM/Llama-2-7B-Chat-text2code")
tokenizer = AutoTokenizer.from_pretrained("SwastikM/Llama-2-7B-Chat-text2code")
inputs = tokenizer(instruction, return_tensors="pt").input_ids.to('cuda')
outputs = model.generate(inputs, max_new_tokens=500, do_sample=False, num_beams=1)
code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(code)
```
## Training Details
### Training Data
[gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
### Training Procedure
HuggingFace Accelerate with Training Loop.
#### Training Hyperparameters
- **Optimizer:** AdamW
- **lr:** 2e-5
- **decay:** linear
- **batch_size:** 4
- **gradient_accumulation_steps:** 8
- **global_step:** 625
#### Hardware
- **GPU:** P100
## Additional Information
- ***Github:*** [Repository]()
- ***Intro to quantization:*** [Blog](https://huggingface.co/blog/merve/quantization)
- ***Emergent Feature:*** [Academic](https://timdettmers.com/2022/08/17/llm-int8-and-emergent-features)
- ***GPTQ Paper:*** [GPTQ](https://arxiv.org/pdf/2210.17323)
- ***BITSANDBYTES and further*** [LLM.int8()](https://arxiv.org/pdf/2208.07339)
## Acknowledgment
Thanks to [@AMerve Noyan](https://huggingface.co/blog/merve/quantization) for precise intro.
Thanks to [@HuggungFace Team](https://colab.research.google.com/drive/1_TIrmuKOFhuRRiTWN94iLKUFu6ZX4ceb?usp=sharing#scrollTo=vT0XjNc2jYKy) for coding guide on gptq.
## Model Card Authors
Swastik Maiti