|
--- |
|
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 |
|
co2_eq_emissions: |
|
emissions: 1190 |
|
source: >- |
|
Quantifying the Carbon Emissions of Machine Learning |
|
https://mlco2.github.io/impact#compute |
|
training_type: finetuning |
|
hardware_used: 1 P100 16GB GPU |
|
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 adapater with GPTQ quantized model is not yet supported. |
|
So instead of loading a single finetuned model, we need to load the base |
|
model and merge the finetuned adapter on top. |
|
``` |
|
|
|
```python |
|
instruction = """"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) |
|
``` |
|
|
|
### Size Comparison |
|
|
|
The table shows comparison VRAM requirements for loading and training |
|
of FP16 Base Model and 4bit GPTQ quantized model with PEFT. |
|
The value for base model referenced from [Model Memory Calculator](https://huggingface.co/docs/accelerate/main/en/usage_guides/model_size_estimator) |
|
from HuggingFace |
|
|
|
|
|
|
|
|
|
| Model | Total Size | Training Using Adam | |
|
| ------------------------|-------------| --------------------| |
|
| Base Model | 12.37 GB | 49.48 GP | |
|
| 4bitQuantized+PEFT | 3.90 GB | 11 GB | |
|
|
|
|
|
## Training Details |
|
|
|
### Training Data |
|
|
|
****Dataset:****[gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) |
|
|
|
Trained on `instruction` column of 20,000 randomly shuffled data. |
|
|
|
### 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 |
|
|
|
LoraConfig |
|
- ***r:*** 8 |
|
- ***lora_alpha:*** 32 |
|
- ***target_modules:*** ["k_proj","o_proj","q_proj","v_proj"] |
|
- ***lora_dropout:*** 0.05 |
|
|
|
|
|
#### 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 the notebook on gptq. |
|
|
|
|
|
## Model Card Authors |
|
|
|
Swastik Maiti |
