--- license: mit --- ## Model Downloads You can download Ring-1T from the following table. If you are located in mainland China, we also provide the model on ModelScope to speed up the download process. | **Model** | **Context Length** | **Download** | | :-------: | :----------------: | :-------------------------------------------------------------------------------------------------------------------------------------------: | | Ring-1T | 64K -> 128K (YaRN) | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-1T) [🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ring-1T) | | Ring-1T-FP8 | 64K -> 128K (YaRN) | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-1T-FP8) [🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ring-1T-FP8) | Note: If you are interested in previous version, please visit the past model collections in [Huggingface](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI). ## Quickstart ### 🚀 Try Online **TODO** You can experience Ring-1T online at: [ZenMux](https://zenmux.ai/inclusionai/ring-1t?utm_source=hf_inclusionAI) ### 🔌 API Usage You can also use Ring-1T through API calls: ```python from openai import OpenAI # 1. Initialize the OpenAI client client = OpenAI( # 2. Point the base URL to the ZenMux endpoint base_url="https://zenmux.ai/api/v1", # 3. Replace with the API Key from your ZenMux user console api_key="", ) # 4. Make a request completion = client.chat.completions.create( # 5. Specify the model to use in the format "provider/model-name" model="inclusionai/ring-1t", messages=[ { "role": "user", "content": "What is the meaning of life?" } ] ) print(completion.choices[0].message.content) ``` ### 🤗 Hugging Face Transformers Here is a code snippet to show you how to use the chat model with `transformers`: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ring-1T" model = AutoModelForCausalLM.from_pretrained( model_name, dtype="auto", device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### 🤖 ModelScope If you're in mainland China, we strongly recommend you to use our model from 🤖 ModelScope. ## Deployment ### vLLM vLLM supports offline batched inference or launching an OpenAI-Compatible API Service for online inference. #### Environment Preparation ```bash pip install vllm==0.11.0 ``` #### Offline Inference: ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ring-1T") sampling_params = SamplingParams(temperature=1.2, top_p=0.8, repetition_penalty=1.0, max_tokens=65536) llm = LLM(model="inclusionAI/Ring-1T", dtype='bfloat16', trust_remote_code=True) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) outputs = llm.generate([text], sampling_params) ``` #### Online Inference: ```bash vllm serve inclusionAI/Ring-1T \ --tensor-parallel-size 32 \ --pipeline-parallel-size 1 \ --trust-remote-code \ --gpu-memory-utilization 0.90 # This is only an example, please adjust arguments according to your actual environment. ``` To handle long context in vLLM using YaRN, we need to follow these two steps: 1. Add a `rope_scaling` field to the model's `config.json` file, for example: ```json { ..., "rope_scaling": { "factor": 2.0, "original_max_position_embeddings": 65536, "type": "yarn" } } ``` 2. Use an additional parameter `--max-model-len` to specify the desired maximum context length when starting the vLLM service. For detailed guidance, please refer to the vLLM [`instructions`](https://docs.vllm.ai/en/latest/). ### SGLang #### Environment Preparation We will later submit our model to SGLang official release, now we can prepare the environment following steps: ```shell pip3 install sglang==0.5.2rc0 sgl-kernel==0.3.7.post1 ``` You can use docker image as well: ```shell docker pull lmsysorg/sglang:v0.5.2rc0-cu126 ``` Then you should apply patch to sglang installation: ```bash # patch command is needed, run `yum install -y patch` if needed patch -d `python -c 'import sglang;import os; print(os.path.dirname(sglang.__file__))'` -p3 < inference/sglang/bailing_moe_v2.patch ``` #### Run Inference BF16 and FP8 models are supported by SGLang now, it depends on the dtype of the model in ${MODEL_PATH}. They both share the same command in the following: - Start server: ```bash python -m sglang.launch_server \ --model-path $MODEL_PATH \ --host 0.0.0.0 --port $PORT \ --trust-remote-code \ --attention-backend fa3 # This is only an example, please adjust arguments according to your actual environment. ``` MTP is supported for base model, and not yet for chat model. You can add parameter `--speculative-algorithm NEXTN` to start command. - Client: ```shell curl -s http://localhost:${PORT}/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}' ``` More usage can be found [here](https://docs.sglang.ai/basic_usage/send_request.html)