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--- |
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library_name: peft |
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base_model: TheBloke/Llama-2-7b-Chat-GPTQ |
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pipeline_tag: text-generation |
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inference: false |
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license: openrail |
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language: |
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- en |
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datasets: |
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- flytech/python-codes-25k |
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co2_eq_emissions: |
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emissions: 1190 |
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source: >- |
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Quantifying the Carbon Emissions of Machine Learning |
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https://mlco2.github.io/impact#compute |
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training_type: finetuning |
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hardware_used: 1 P100 16GB GPU |
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tags: |
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- text2code |
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- LoRA |
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- GPTQ |
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- Llama-2-7B-Chat |
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- text2python |
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- instruction2code |
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--- |
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# Llama-2-7b-Chat-GPTQ fine-tuned on PYTHON-CODES-25K |
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Generate Python code that accomplishes the task instructed. |
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## LoRA Adpater Head |
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### Description |
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Parameter Efficient Finetuning(PEFT) a 4bit quantized Llama-2-7b-Chat from TheBloke/Llama-2-7b-Chat-GPTQ on flytech/python-codes-25k dataset. |
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- **Language(s) (NLP):** English |
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- **License:** openrail |
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- **Qunatization:** GPTQ 4bit |
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- **PEFT:** LoRA |
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- **Finetuned from model [TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ)** |
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- **Dataset:** [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k) |
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## Intended uses & limitations |
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Addressing the efficay of Quantization and PEFT. Implemented as a personal Project. |
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### How to use |
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``` |
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The quantized model is finetuned as PEFT. We have the trained Adapter. |
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Merging LoRA adapater with GPTQ quantized model is not yet supported. |
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So instead of loading a single finetuned model, we need to load the base |
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model and merge the finetuned adapter on top. |
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``` |
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```python |
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instruction = """"Help me set up my daily to-do list!"""" |
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``` |
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```python |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM |
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config = PeftConfig.from_pretrained("SwastikM/Llama-2-7B-Chat-text2code") #PEFT Config |
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model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7b-Chat-GPTQ") #Loading the Base Model |
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model = PeftModel.from_pretrained(model, "SwastikM/Llama-2-7B-Chat-text2code") #Combining Trained Adapter with Base Model |
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tokenizer = AutoTokenizer.from_pretrained("SwastikM/Llama-2-7B-Chat-text2code") |
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inputs = tokenizer(instruction, return_tensors="pt").input_ids.to('cuda') |
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outputs = model.generate(inputs, max_new_tokens=500, do_sample=False, num_beams=1) |
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code = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(code) |
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``` |
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### Size Comparison |
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The table shows comparison VRAM requirements for loading and training |
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of FP16 Base Model and 4bit GPTQ quantized model with PEFT. |
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The value for base model referenced from [Model Memory Calculator](https://huggingface.co/docs/accelerate/main/en/usage_guides/model_size_estimator) |
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from HuggingFace |
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| Model | Total Size | Training Using Adam | |
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| ------------------------|-------------| --------------------| |
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| Base Model | 12.37 GB | 49.48 GP | |
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| 4bitQuantized+PEFT | 3.90 GB | 11 GB | |
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## Training Details |
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### Training Data |
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****Dataset:****[gretelai/synthetic_text_to_sql](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql) |
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Trained on `instruction` column of 20,000 randomly shuffled data. |
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### Training Procedure |
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HuggingFace Accelerate with Training Loop. |
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#### Training Hyperparameters |
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- **Optimizer:** AdamW |
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- **lr:** 2e-5 |
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- **decay:** linear |
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- **batch_size:** 4 |
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- **gradient_accumulation_steps:** 8 |
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- **global_step:** 625 |
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LoraConfig |
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- ***r:*** 8 |
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- ***lora_alpha:*** 32 |
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- ***target_modules:*** ["k_proj","o_proj","q_proj","v_proj"] |
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- ***lora_dropout:*** 0.05 |
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#### Hardware |
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- **GPU:** P100 |
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## Additional Information |
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- ***Github:*** [Repository]() |
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- ***Intro to quantization:*** [Blog](https://huggingface.co/blog/merve/quantization) |
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- ***Emergent Feature:*** [Academic](https://timdettmers.com/2022/08/17/llm-int8-and-emergent-features) |
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- ***GPTQ Paper:*** [GPTQ](https://arxiv.org/pdf/2210.17323) |
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- ***BITSANDBYTES and further*** [LLM.int8()](https://arxiv.org/pdf/2208.07339) |
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## Acknowledgment |
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Thanks to [@AMerve Noyan](https://huggingface.co/blog/merve/quantization) for precise intro. |
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Thanks to [@HuggungFace Team](https://colab.research.google.com/drive/1_TIrmuKOFhuRRiTWN94iLKUFu6ZX4ceb?usp=sharing#scrollTo=vT0XjNc2jYKy) for the notebook on gptq. |
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## Model Card Authors |
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Swastik Maiti |
