Upload LoRA adapter

README.md

@@ -8,105 +8,19 @@ tags:
 datasets:
 - vicgalle/alpaca-gpt4
 ---
-# FlowerTune LLM on General NLP Dataset
 
-This directory conducts federated instruction tuning with pretrained language models on a general NLP dataset [vicgalle/alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4).
-We use [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the dataset.
-Flower's Simulation Engine is used to simulate the LLM fine-tuning process in a federated way,
-which allows users to perform the training on a single GPU.
+# FlowerTune LoRA Model
 
-## Links
-
-- **GitHub Repository**: [https://github.com/zjudai/flwr-nlp](https://github.com/zjudai/flwr-nlp)
-- **Hugging Face Homepage**: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune)
-- **FlowerTune Collection**: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)
-
-## Experimental Setup
-
-The dataset is divided into 20 partitions in an IID fashion, a partition is assigned to each ClientApp.
-We randomly sample a fraction (0.1) of the total nodes to participate in each round, for a total of `10` rounds.
-All settings are defined in `pyproject.toml`.
-
-## Methodology
-
-This baseline performs federated LLM fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) using the [🤗PEFT](https://huggingface.co/docs/peft/en/index) library.
-The clients' models are aggregated with `FedAvg` strategy.
-This provides a baseline performance for general NLP tasks with evaluation on MMLU benchmark.
-
-### Example: Qwen2.5-7B-Instruct
-
-For example, with the **Qwen/Qwen2.5-7B-Instruct** model we adopted the following fine-tuning methodology:
-
-- **Precision**: `bf16` for model weights.
-- **Quantization**: `4-bit` quantization for reduced memory usage.
-- **LoRA Configuration**:
-  - Rank (r): `32`
-  - Alpha: `64`
-- **Training Configuration**:
-  - Batch size: `8`
-  - Maximum number of steps: `10`
-  - Total number of rounds: `10`
-  - Fraction fit per round: `0.1`
-- **Learning Rate Scheduler**:
-  - Maximum LR: `5e-5`
-  - Minimum LR: `1e-6`
-  - Constant learning rate scheduler over steps
-- **Strategy**: `FedAvg`
-
-## Environment and Execution
-
-### Environment Setup
-
-Project dependencies are defined in `pyproject.toml`. Install them in an activated Python environment with:
+This is a LoRA adapter for meta-llama/Llama-3.2-1B-Instruct fine-tuned with Flower federated learning framework on a general NLP dataset.
 
-```shell
-python -m pip install --upgrade pip wheel setuptools packaging
+## Training Details
 
-pip install -e .
-```
+- Dataset: vicgalle/alpaca-gpt4
+- Training method: Federated LoRA fine-tuning with FlowerTune
+- Framework: Flower
 
-### Running the Training and Evaluation
+This model is a LoRA adapter fine-tuned on meta-llama/Llama-3.2-1B-Instruct using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance.
 
-We use a wrapper script `run_all_experiments.sh` to handle both training and evaluation processes:
-
-```bash
-# Example of running experiments
-./run_all_experiments.sh --model Qwen/Qwen2.5-7B-Instruct --task general_nlp
-```
-
-The wrapper script sets up the proper environment, including:
-- Activating the conda environment
-- Setting up proxy configurations if needed
-- Executing the main experiment runner script with the provided parameters
-
-The actual experiment workflow is implemented in `run_experiments.py`, which is called by the wrapper script.
-
-### Model Saving
-
-The global PEFT model checkpoints are saved every 5 rounds after aggregation on the server side as default, which can be specified with `train.save-every-round` under [tool.flwr.app.config] entry in `pyproject.toml`.
-
-## Evaluation Results
-
-The evaluation was conducted on the MMLU (Massive Multitask Language Understanding) benchmark, which tests knowledge across various domains:
-
-| **Model** | **STEM** | **Social Sciences** | **Humanities** | **Average** |
-|-----------|----------|---------------------|----------------|-------------|
-| Qwen/Qwen2.5-7B-Instruct | 52.52% | 79.27% | 60.32% | 64.04% |
-| Qwen/Qwen2.5-1.5B-Instruct | 47.13% | 62.30% | 50.54% | 53.32% |
-| mistralai/Mistral-7B-Instruct-v0.3 | 29.94% | 54.27% | 44.93% | 43.05% |
-| meta-llama/Llama-3.1-8B-Instruct | 22.87% | 39.55% | 32.05% | 31.49% |
-| mistralai/Mistral-7B-v0.3 | 12.59% | 31.13% | 27.10% | 23.61% |
-| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | 14.18% | 21.61% | 21.91% | 19.23% |
-| meta-llama/Llama-3.2-1B-Instruct | 12.88% | 17.61% | 6.16% | 12.22% |
-| google/gemma-3-1b-it | 0.10% | 0.49% | 0.15% | 0.24% |
-| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | 0.54% | 0.00% | 0.04% | 0.19% |
-
-## Hardware Details
-
-For this experiment, I utilized a GPU-enabled virtual machine.
-
-| **Component** | **Specification**    |
-|---------------|----------------------|
-| **GPU**       | 1 × GPU with 16+ GB  |
-| **vCPUs**     | 6                    |
-| **Memory**    | 16+ GB               |
+## Links
+- FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune)
+- FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)