README.md

---
language:
- en
license: apache-2.0
library_name: peft
tags:
- text-generation
widget:
- text: 'Below is an instruction that describes a task, paired with an input that
    provides further context. Write a response that appropriately completes the request.

    ### Instruction: Generate an SQL statement to add a row in the customers table
    where the columns are name, address, and city.

    ### Input: name = John, address = 123 Main Street, city = Winter Park

    ### Response:

    '
inference:
  parameters:
    temperature: 0.1
    max_new_tokens: 1024
base_model: meta-llama/Llama-2-7b-hf
---
# QLoRA weights using Llama-2-7b for the Code Alpaca Dataset

# Fine-Tuning on Predibase

This model was fine-tuned using [Predibase](https://predibase.com/), the first low-code AI platform for engineers.
I fine-tuned base Llama-2-7b using LoRA with 4 bit quantization on a single T4 GPU, which cost approximately $3 to train
on Predibase. Try out our free Predibase trial [here](https://predibase.com/free-trial).

Dataset and training parameters are borrowed from: https://github.com/sahil280114/codealpaca,
but all of these parameters including DeepSpeed can be directly used with [Ludwig](https://ludwig.ai/latest/), the open-source
toolkit for LLMs that Predibase is built on.

Co-trained by: [Infernaught](https://huggingface.co/Infernaught)

# How To Use The Model

To use these weights:
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

# Load base model in 4 bit
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", load_in_4bit=True)

# Wrap model with pretrained model weights
config = PeftConfig.from_pretrained("arnavgrg/codealpaca-qlora")
model = PeftModel.from_pretrained(model, "arnavgrg/codealpaca-qlora")
```

Prompt Template:
```
Below is an instruction that describes a task, paired with an input
that provides further context. Write a response that appropriately
completes the request.

### Instruction: {instruction}

### Input: {input}

### Response:
```

## Training procedure

The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16

### Framework versions


- PEFT 0.4.0