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\n 🤗 HuggingFace •\n 🤖 ModelScope •\n 🟣 wisemodel \n
\n 👾 Discord •\n 🐤 Twitter •\n 💬 WeChat \n
\n 📝 Paper •\n 💪 Tech Blog •\n 🙌 FAQ •\n 📗 Learning Hub\n

\n\n---\n\n- [Intro](#intro)\n- [News](#news)\n- [Quick Start](#quick-start)\n- [Web Demo](#web-demo)\n- [Deployment](#deployment)\n- [Fine-tuning](#fine-tuning)\n- [API](#api)\n- [License](#license)\n\n## Intro\n\nYi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. \n\nCompared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. \n\nYi-1.5 comes in 3 model sizes: 34B, 9B, and 6B. For model details and benchmarks, see [Model Card](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8).\n\n## News\n\n- 2024-05-13: The Yi-1.5 series models are open-sourced, further improving coding, math, reasoning, and instruction-following abilities. \n\n## Requirements\n\n- Make sure Python 3.10 or a later version is installed.\n\n- Set up the environment and install the required packages.\n\n ```bash\n pip install -r requirements.txt\n ```\n \n- Download the Yi-1.5 model from [Hugging Face](https://huggingface.co/01-ai), [ModelScope](https://www.modelscope.cn/organization/01ai/), or [WiseModel](https://wisemodel.cn/organization/01.AI).\n\n## Quick Start\n\nThis tutorial runs Yi-1.5-34B-Chat locally on an A800 (80G).\n\n> **💡 Tip**: If you want to get started with the Yi model and explore different methods for inference, check out the [Yi Cookbook](https://github.com/01-ai/Yi/tree/main/Cookbook).\n\n ```python\n from transformers import AutoModelForCausalLM, AutoTokenizer\n \n model_path = ''\n \n tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)\n \n # Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.\n model = AutoModelForCausalLM.from_pretrained(\n model_path,\n device_map=\"auto\",\n torch_dtype='auto'\n ).eval()\n \n # Prompt content: \"hi\"\n messages = [\n {\"role\": \"user\", \"content\": \"hi\"}\n ]\n \n input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors='pt')\n output_ids = model.generate(input_ids.to('cuda'), eos_token_id=tokenizer.eos_token_id)\n response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)\n \n # Model response: \"Hello! How can I assist you today?\"\n print(response)\n ```\n\n### Ollama \n\n\n\n\nYou can run Yi-1.5 models on Ollama locally.\n\n1. After [installing Ollama](https://github.com/ollama/ollama/tree/main/docs), you can start the Ollama service. Note that keep this service running while you use Ollama.\n \n ```python\n ollama serve\n ```\n\n2. Run Yi-1.5 models. For more Yi models supported by Ollama, see [Yi tags](https://ollama.com/library/yi/tags).\n \n ```python\n ollama run yi:v1.5\n ```\n\n3. Chat with Yi-1.5 via OpenAI-compatible API. For more details on how to use Yi-1.5 via OpenAI API and REST API on Ollama, see [Ollama docs](https://github.com/ollama/ollama/tree/main/docs).\n\n ```python\n from openai import OpenAI\n client = OpenAI(\n base_url='http://localhost:11434/v1/',\n api_key='ollama', # required but ignored\n )\n chat_completion = client.chat.completions.create(\n messages=[\n {\n 'role': 'user',\n 'content': 'What is your name',\n }\n ],\n model='yi:1.5',\n )\n ```\n\n## Deployment\n\nPrerequisites: Before deploying Yi-1.5 models, make sure you meet the [software and hardware requirements](https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#software-requirements). \n\n### vLLM\n\nPrerequisites: Download the latest version of [vLLM](https://docs.vllm.ai/en/latest/getting_started/installation.html).\n\n1. Start the server with a chat model.\n\n ```bash\n python -m vllm.entrypoints.openai.api_server --model 01-ai/Yi-1.5-9B-Chat --served-model-name Yi-1.5-9B-Chat\n ```\n\n2. Use the chat API.\n\n - HTTP\n\n ```bash\n curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Yi-1.5-9B-Chat\",\n \"messages\": [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"}\n ]\n }'\n ```\n\n - Python client\n\n ```python\n from openai import OpenAI\n # Set OpenAI's API key and API base to use vLLM's API server.\n openai_api_key = \"EMPTY\"\n openai_api_base = \"http://localhost:8000/v1\"\n \n client = OpenAI(\n api_key=openai_api_key,\n base_url=openai_api_base,\n )\n \n chat_response = client.chat.completions.create(\n model=\"Yi-1.5-9B-Chat\",\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"Tell me a joke.\"},\n ]\n )\n print(\"Chat response:\", chat_response)\n ```\n\n## Web Demo\n\nYou can activate Yi-1.5-34B-Chat through the [huggingface chat ui](https://huggingface.co/chat/settings/01-ai/Yi-1.5-34B-Chat/) then experience it.\n\nOr you can build it locally by yourself, as follows:\n```\npython demo/web_demo.py -c \n```\n\n## Fine-tuning\n\nYou can use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), [Swift](https://github.com/modelscope/swift), [XTuner](https://github.com/InternLM/xtuner), and [Firefly](https://github.com/yangjianxin1/Firefly) for fine-tuning. These frameworks all support fine-tuning the Yi series models.\n\n## API\n\nYi APIs are OpenAI-compatible and provided at [Yi Platform](https://platform.lingyiwanwu.com/). Sign up to get free tokens, and you can also pay-as-you-go at a competitive price. Additionally, Yi APIs are also deployed on [Replicate](https://replicate.com/search?query=01+ai) and [OpenRouter](https://openrouter.ai/models?q=01%20ai). \n\n## License\n\nThe code and weights of the Yi-1.5 series models are distributed under the [Apache 2.0 license](https://github.com/01-ai/Yi/blob/main/LICENSE).\n\nIf you create derivative works based on this model, please include the following attribution in your derivative works:\n\n This work is a derivative of [The Yi-1.5 Series Model You Base On] by 01.AI, used under the Apache 2.0 License.\n\n

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\n" }, { "path": "demo/web_demo.py", "content": "import gradio as gr\nfrom argparse import ArgumentParser\nfrom threading import Thread\nfrom transformers import (\n AutoModelForCausalLM,\n AutoTokenizer,\n TextIteratorStreamer,\n)\n\n\ndef respond(\n message,\n history: list[tuple[str, str]],\n system_message,\n max_tokens,\n temperature,\n top_p,\n):\n messages = [{\"role\": \"system\", \"content\": system_message}]\n\n for val in history:\n if val[0]:\n messages.append({\"role\": \"user\", \"content\": val[0]})\n if val[1]:\n messages.append({\"role\": \"assistant\", \"content\": val[1]})\n\n messages.append({\"role\": \"user\", \"content\": message})\n\n model_inputs = tokenizer.apply_chat_template(\n messages, add_generation_prompt=True, tokenize=True, return_tensors=\"pt\"\n ).to(next(model.parameters()).device)\n\n streamer = TextIteratorStreamer(\n tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True\n )\n generate_kwargs = {\n \"input_ids\": model_inputs,\n \"streamer\": streamer,\n \"max_new_tokens\": max_tokens,\n \"do_sample\": True,\n \"top_p\": top_p,\n \"temperature\": temperature,\n \"repetition_penalty\": 1.2,\n \"eos_token_id\": [tokenizer.eos_token_id],\n }\n t = Thread(target=model.generate, kwargs=generate_kwargs)\n t.start()\n response = \"\"\n for new_token in streamer:\n if new_token != \"\":\n response += new_token\n yield response\n\n\n\"\"\"\nFor information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface\n\"\"\"\ndemo = gr.ChatInterface(\n respond,\n additional_inputs=[\n gr.Textbox(value=\"You are a friendly Chatbot.\", label=\"System message\"),\n gr.Slider(minimum=1, maximum=2048, value=512, step=1, label=\"Max new tokens\"),\n gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label=\"Temperature\"),\n gr.Slider(\n minimum=0.1,\n maximum=1.0,\n value=0.95,\n step=0.05,\n label=\"Top-p (nucleus sampling)\",\n ),\n ],\n)\n\n\nif __name__ == \"__main__\":\n parser = ArgumentParser()\n parser.add_argument(\n \"-c\",\n \"--checkpoint-path\",\n type=str,\n default=\"01-ai/Yi-1.5-34B-Chat\",\n help=\"Checkpoint name or path, default to %(default)r\",\n )\n parser.add_argument(\n \"--share\",\n action=\"store_true\",\n default=False,\n help=\"Create a publicly shareable link for the interface.\",\n )\n parser.add_argument(\n \"--inbrowser\",\n action=\"store_true\",\n default=True,\n help=\"Automatically launch the interface in a new tab on the default browser.\",\n )\n parser.add_argument(\n \"--server-port\", type=int, default=8000, help=\"Demo server port.\"\n )\n parser.add_argument(\n \"--server-name\", type=str, default=\"127.0.0.1\", help=\"Demo server name.\"\n )\n\n args = parser.parse_args()\n\n tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=False)\n model = AutoModelForCausalLM.from_pretrained(\n args.checkpoint_path, device_map=\"auto\", torch_dtype=\"auto\"\n ).eval()\n\n demo.launch(\n server_name=args.server_name,\n server_port=args.server_port,\n inbrowser=args.inbrowser,\n share=args.share,\n )\n" }, { "path": "requirements.txt", "content": "transformers>=4.36.2\ngradio>=4.13.0\ntorch>=2.0.1,<=2.3.0\nsentencepiece\naccelerate" } ]