Merged InstructionTuning and RerankFinetuning into Finetuning

InstructionTuning/README.md → Finetuning/README.md

@@ -1,30 +1,43 @@
-# Instruction Tuning
+# Finetuning
 
-Instruction tuning is the process of further training LLMs on a dataset consisting of (instruction, output) pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. This implementation deploys a Ray cluster for the task.
+This example includes instruction tuning and rerank model finetuning. Instruction tuning is the process of further training LLMs on a dataset consisting of (instruction, output) pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. Rerank model finetuning is the process of further training rerank model on a dataset for improving its capability on specific field. The implementation of this example deploys a Ray cluster for the task.
 
-## Deploy Instruction Tuning Service
+## Deploy Finetuning Service
 
-### Deploy Instruction Tuning Service on Xeon
+### Deploy Finetuning Service on Xeon
 
 Refer to the [Xeon Guide](./docker_compose/intel/cpu/xeon/README.md) for detail.
 
-### Deploy Instruction Tuning Service on Gaudi
+### Deploy Finetuning Service on Gaudi
 
 Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) for detail.
 
-## Consume Instruction Tuning Service
+## Consume Finetuning Service
 
 ### 1. Upload a training file
 
+#### Instruction tuning dataset example
+
 Download a training file `alpaca_data.json` and upload it to the server with below command, this file can be downloaded in [here](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json):
 
 ```bash
 # upload a training file
 curl http://${your_ip}:8015/v1/files -X POST -H "Content-Type: multipart/form-data" -F "file=@./alpaca_data.json" -F purpose="fine-tune"
 ```
 
+#### Rerank model finetuning dataset example
+
+Download a toy example training file `toy_finetune_data.jsonl` and upload it to the server with below command, this file can be downloaded in [here](https://github.com/FlagOpen/FlagEmbedding/blob/JUNJIE99-patch-1/examples/finetune/toy_finetune_data.jsonl):
+
+```bash
+# upload a training file
+curl http://${your_ip}:8015/v1/files -X POST -H "Content-Type: multipart/form-data" -F "file=@./toy_finetune_data.jsonl" -F purpose="fine-tune"
+```
+
 ### 2. Create fine-tuning job
 
+#### Instruction tuning
+
 After a training file like `alpaca_data.json` is uploaded, use the following command to launch a finetuning job using `meta-llama/Llama-2-7b-chat-hf` as base model:
 
 ```bash
@@ -40,6 +53,25 @@ curl http://${your_ip}:8015/v1/fine_tuning/jobs \
 
 The outputs of the finetune job (adapter_model.safetensors, adapter_config,json... ) are stored in `/home/user/comps/finetuning/src/output` and other execution logs are stored in `/home/user/ray_results`
 
+#### Rerank model finetuning
+
+After a training file `toy_finetune_data.jsonl` is uploaded, use the following command to launch a finetuning job using `BAAI/bge-reranker-large` as base model:
+
+```bash
+# create a finetuning job
+curl http://${your_ip}:8015/v1/fine_tuning/jobs \
+  -X POST \
+  -H "Content-Type: application/json" \
+  -d '{
+    "training_file": "toy_finetune_data.jsonl",
+    "model": "BAAI/bge-reranker-large",
+    "General":{
+      "task":"rerank",
+      "lora_config":null
+    }
+  }'
+```
+
 ### 3. Manage fine-tuning job
 
 Below commands show how to list finetuning jobs, retrieve a finetuning job, cancel a finetuning job and list checkpoints of a finetuning job.

InstructionTuning/docker_compose/intel/cpu/xeon/README.md → Finetuning/docker_compose/intel/cpu/xeon/README.md

@@ -1,6 +1,6 @@
-# Deploy Instruction Tuning Service on Xeon
+# Deploy Finetuning Service on Xeon
 
-This document outlines the deployment process for a Instruction Tuning Service utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice on Intel Xeon server. The steps include Docker image creation, container deployment. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
+This document outlines the deployment process for a finetuning Service utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice on Intel Xeon server. The steps include Docker image creation, container deployment. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
 
 ## 🚀 Build Docker Images
 

InstructionTuning/docker_compose/intel/hpu/gaudi/README.md → Finetuning/docker_compose/intel/hpu/gaudi/README.md

@@ -1,6 +1,6 @@
-# Deploy Instruction Tuning Service on Gaudi
+# Deploy Finetuning Service on Gaudi
 
-This document outlines the deployment process for a Instruction Tuning Service utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice on Intel Gaudi server. The steps include Docker image creation, container deployment. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
+This document outlines the deployment process for a finetuning Service utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice on Intel Gaudi server. The steps include Docker image creation, container deployment. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
 
 ## 🚀 Build Docker Images