metadata
library_name: peft
tags:
- text-generation
license: apache-2.0
metrics:
- loss
language:
- en
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
QLoRA weights using Llama-2-7b for the Code Alpaca Dataset
This model was fine-tuned using Predibase, 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.
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, the open-source toolkit for LLMs that Predibase is built on.
To use these weights:
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
config = PeftConfig.from_pretrained("arnavgrg/codealpaca-qlora")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
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