commit c5c8aeacc31f7ac485b620f3ac7401b9c0ca3d01 Author: ModelHub XC Date: Wed May 27 04:22:12 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: NCSOFT/Llama-VARCO-8B-Instruct Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..9050b52 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,32 @@ +LLAMA 3.1 COMMUNITY LICENSE AGREEMENT +Llama 3.1 Version Release Date: July 23, 2024 + +“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. +“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1 distributed by Meta at https://llama.com/docs/overview. +“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. +“Llama 3.1” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.com/llama-downloads. +“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion thereof) made available under this Agreement. +“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. \ No newline at end of file diff --git a/Llama-3-1-Varco-8B.ipynb b/Llama-3-1-Varco-8B.ipynb new file mode 100644 index 0000000..284018d --- /dev/null +++ b/Llama-3-1-Varco-8B.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deploy Llama-VARCO-8B-Instruct Model from AWS Marketplace \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Llama-VARCO-8B-Instruct is a generative model built with Llama, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.\n", + "\n", + "This sample notebook shows you how to deploy [Llama-VARCO-8B-Instruct](https://aws.amazon.com/marketplace/pp/prodview-pynin2e23lb3e) using Amazon SageMaker.\n", + "\n", + "> **Note**: This is a reference notebook and it cannot run unless you make changes suggested in the notebook.\n", + "\n", + "## Pre-requisites:\n", + "1. **Note**: This notebook contains elements which render correctly in Jupyter interface. Open this notebook from an Amazon SageMaker Notebook Instance or Amazon SageMaker Studio.\n", + "1. Ensure that IAM role used has **AmazonSageMakerFullAccess**\n", + "1. To deploy this ML model successfully, ensure that:\n", + " 1. Either your IAM role has these three permissions and you have authority to make AWS Marketplace subscriptions in the AWS account used: \n", + " 1. **aws-marketplace:ViewSubscriptions**\n", + " 1. **aws-marketplace:Unsubscribe**\n", + " 1. **aws-marketplace:Subscribe** \n", + "\n", + "## Contents:\n", + "1. [Subscribe to the model package](#1.-Subscribe-to-the-model-package)\n", + "2. [Create an endpoint and perform real-time inference](#2.-Create-an-endpoint-and-perform-real-time-inference)\n", + "3. [Clean-up](#3.-Clean-up)\n", + "\n", + " \n", + "\n", + "## Usage instructions\n", + "You can run this notebook one cell at a time (By using Shift+Enter for running a cell)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Subscribe to the model package" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "To subscribe to the model package:\n", + "1. Open the model package [listing page](https://aws.amazon.com/marketplace/pp/prodview-pynin2e23lb3e)\n", + "1. On the AWS Marketplace listing, click on the **Continue to subscribe** button.\n", + "1. On the **Subscribe to this software** page, review and click on **\"Accept Offer\"** if you and your organization agrees with EULA, pricing, and support terms. \n", + "1. Once you click on **Continue to configuration button** and then choose a **region**, you will see a **Product Arn** displayed. This is the model package ARN that you need to specify while creating a deployable model using Boto3. Copy the ARN corresponding to your region and specify the same in the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_package_arn = \"arn:aws:sagemaker:us-west-2:594846645681:model-package/llama-varco-8b-ist-bedrock-37339dbb44f23f488e24f8671eaa0494\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import base64\n", + "import json\n", + "import uuid\n", + "from sagemaker import ModelPackage\n", + "import sagemaker as sage\n", + "from sagemaker import get_execution_role\n", + "from sagemaker import ModelPackage\n", + "import boto3\n", + "from IPython.display import Image\n", + "from PIL import Image as ImageEdit\n", + "import numpy as np\n", + "import io" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "role = get_execution_role()\n", + "\n", + "sagemaker_session = sage.Session()\n", + "\n", + "bucket = sagemaker_session.default_bucket()\n", + "runtime = boto3.client(\"runtime.sagemaker\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Create an endpoint and perform real-time inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to understand how real-time inference with Amazon SageMaker works, see [Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-hosting.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_name = \"Llama-VARCO-8B-Instruct\"\n", + "\n", + "content_type = \"application/json\"\n", + "\n", + "real_time_inference_instance_type = (\n", + " \"ml.g5.12xlarge\"\n", + ")\n", + "batch_transform_inference_instance_type = (\n", + " \"ml.g4dn.12xlarge\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A.Create an endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create a deployable model from the model package.\n", + "model = ModelPackage(\n", + " role=role, model_package_arn=model_package_arn, sagemaker_session=sagemaker_session\n", + ")\n", + "\n", + "# Deploy the model\n", + "predictor = model.deploy(1, real_time_inference_instance_type, endpoint_name=model_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once endpoint has been created, you would be able to perform real-time inference." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### B.Create input payload" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "input = {\n", + " \"messages\": [\n", + " {\n", + " \"role\":\"user\",\n", + " \"content\":\"안녕 넌 누구야?\"\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### C. Perform real-time inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### C-1. Stream Inference Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class VarcoInferenceStream():\n", + " def __init__(self, sagemaker_runtime, endpoint_name):\n", + " self.sagemaker_runtime = sagemaker_runtime\n", + " self.endpoint_name = endpoint_name\n", + "\n", + " def stream_inference(self, request_body):\n", + " # Gets a streaming inference response\n", + " # from the specified model endpoint:\n", + " response = self.sagemaker_runtime\\\n", + " .invoke_endpoint_with_response_stream(\n", + " EndpointName=self.endpoint_name,\n", + " Body=json.dumps(request_body),\n", + " ContentType=\"application/json\"\n", + " )\n", + " # Gets the EventStream object returned by the SDK:\n", + " for body in response[\"Body\"]:\n", + " raw = body['PayloadPart']['Bytes']\n", + " yield raw.decode()\n", + "\n", + "\n", + "sm_runtime = boto3.client(\"sagemaker-runtime\")\n", + "varco_inference_stream = VarcoInferenceStream(sm_runtime, model_name)\n", + "stream = varco_inference_stream.stream_inference(input)\n", + "for part in stream:\n", + " print(part, end='')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## 3. Clean-up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that you have successfully performed a real-time inference, you do not need the endpoint any more. You can terminate the endpoint to avoid being charged." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A. Delete the endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.sagemaker_session.delete_endpoint(model_name)\n", + "model.sagemaker_session.delete_endpoint_config(model_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### B. Delete the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.delete_model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### C. Unsubscribe to the listing (optional)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you would like to unsubscribe to the model package, follow these steps. Before you cancel the subscription, ensure that you do not have any [deployable model](https://console.aws.amazon.com/sagemaker/home#/models) created from the model package or using the algorithm. Note - You can find this information by looking at the container name associated with the model. \n", + "\n", + "**Steps to unsubscribe to product from AWS Marketplace**:\n", + "1. Navigate to __Machine Learning__ tab on [__Your Software subscriptions page__](https://aws.amazon.com/marketplace/ai/library?productType=ml&ref_=mlmp_gitdemo_indust)\n", + "2. Locate the listing that you want to cancel the subscription for, and then choose __Cancel Subscription__ to cancel the subscription.\n", + "\n" + ] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "conda_pytorch_p310", + "language": "python", + "name": "conda_pytorch_p310" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notice.txt b/Notice.txt new file mode 100644 index 0000000..0aed9e1 --- /dev/null +++ b/Notice.txt @@ -0,0 +1 @@ +Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..e0f9412 --- /dev/null +++ b/README.md @@ -0,0 +1,76 @@ +--- +language: +- en +- ko +license: llama3.1 +tags: +- llama-3.1 +- ncsoft +- varco +base_model: +- meta-llama/Meta-Llama-3.1-8B +library_name: transformers +--- + +## Llama-VARCO-8B-Instruct + +### About the Model + +**Llama-VARCO-8B-Instruct** is a *generative model* built with Llama, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences. + +- **Developed by:** NC Research, Language Model Team +- **Languages (NLP):** Korean, English +- **License:** LLAMA 3.1 COMMUNITY LICENSE AGREEMENT +- **Base model:** [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) + +## Uses + +### Direct Use + +We recommend to use transformers v4.43.0 or later, as advised for Llama-3.1. + +```python + from transformers import AutoTokenizer, AutoModelForCausalLM + import torch + + model = AutoModelForCausalLM.from_pretrained( + "NCSOFT/Llama-VARCO-8B-Instruct", + torch_dtype=torch.bfloat16, + device_map="auto" + ) + tokenizer = AutoTokenizer.from_pretrained("NCSOFT/Llama-VARCO-8B-Instruct") + + messages = [ + {"role": "system", "content": "You are a helpful assistant Varco. Respond accurately and diligently according to the user's instructions."}, + {"role": "user", "content": "안녕하세요."} + ] + + inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device) + + eos_token_id = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("<|eot_id|>") + ] + + outputs = model.generate( + inputs, + eos_token_id=eos_token_id, + max_length=8192 + ) + + print(tokenizer.decode(outputs[0])) +``` + +## Evaluation + +### LogicKor + +We used the [LogicKor](https://github.com/instructkr/LogicKor) code to measure performance. For the judge model, we used the officially recommended gpt-4-1106-preview. The score includes only the 0-shot evaluation provided in the default. + +| Model | Math | Reasoning | Writing | Coding | Understanding | Grammer | Single turn | Multi turn | Overall | +|--------------|--------|-------------|-----------|----------|-----------------|-----------|---------------|--------------|-----------| +| [Llama-VARCO-8B-Instruct](https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct)| 6.71 / 8.57 | 8.86 / 8.29 | 9.86 / 9.71 | 8.86 / 9.29 | 9.29 / 10.0 | 8.57 / 7.86 | 8.69 | 8.95 | 8.82 | +| [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)| 6.86 / 7.71 | 8.57 / 6.71 | 10.0 / 9.29 | 9.43 / 10.0 | 10.0 / 10.0 | 9.57 / 5.14 | 9.07 | 8.14 | 8.61 | +| [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)| 4.29 / 4.86 | 6.43 / 6.57 | 6.71 / 5.14 | 6.57 / 6.00 | 4.29 / 4.14 | 6.00 / 4.00 | 5.71 | 5.12 | 5.42 | +| [Gemma-2-9B-Instruct](https://huggingface.co/google/gemma-2-9b-it)| 6.14 / 5.86 | 9.29 / 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