jasperyeoh2/llama2-7b-backward-model

Model Details

Model Description

• Developed by: [Jixin Yang @ HKUST]
• Model type: [PEFT (LoRA) fine-tuned LLaMA-2 7B for backward text generation]
• Finetuned from model [optional]: [meta-llama/Llama-2-7b-hf]

Uses

This model is designed for backward text generation - given an output text, it generates the corresponding input.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "jasperyeoh2/llama2-7b-backward-model" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

input_text = "Output text to reverse" inputs = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

• Dataset: OpenAssistant-Guanaco
• Number of examples used: ~3,200
• Task: Instruction Backtranslation (Answer → Prompt)

Training Procedure

Preprocessing [optional]

• Method: PEFT with LoRA (Low-Rank Adaptation)
• Quantization: 4-bit (NF4)
• LoRA config:
  • r: 8
  • alpha: 16
  • target_modules: ["q_proj", "v_proj"]
  • dropout: 0.05
• Max sequence length: 512 tokens
• Epochs: 10
• Batch size: 2
• Gradient accumulation steps: 8
• Effective batch size: 16
• Learning rate: 2e-5
• Scheduler: linear with warmup
• Optimizer: AdamW
• Early stopping: enabled (patience=2)

Metrics

[wandb: https://wandb.ai/jyang577-hong-kong-university-of-science-and-technology/huggingface?nw=nwuserjyang577]

Results

[- Final eval loss: ~1.436

• Final train loss: ~1.4
• Training completed in ~8 epochs]

Compute Infrastructure

• GPU: 1× NVIDIA A800 (80GB)
• CUDA Version: 12.1

Software

• OS: Ubuntu 20.04
• Python: 3.10
• Transformers: 4.38.2
• PEFT: 0.15.1
• Accelerate: 0.28.0
• BitsAndBytes: 0.41.2]

Hardware

NVIDIA A800 GPU

Framework versions

• PEFT 0.15.1