pax_global_header00006660000000000000000000000064146630246320014520gustar00rootroot0000000000000052 comment=e157f2d528a9e996da0625d048ba4be82c8b6394 openi_llm_example/000077500000000000000000000000001466302463200145555ustar00rootroot00000000000000openi_llm_example/.openi-aitemplate000066400000000000000000000060561466302463200200220ustar00rootroot00000000000000[ { "Name": "测试LLM API服务", "Description": "", "JobType": "DEBUG", "Cluster": "C2Net", "ComputeSource": "GPU", "HasInternet": 2, "AccCardsNum": 1, "AccCardType": "A100", "CpuCores": 8, "MemGiB": 50, "GPUMemGiB": 40, "ShareMemGiB": 0, "Image": { "ImageID": "", "ImageName": "", "ImageUrl": "192.168.204.22:5000/default-workspace/99280a9940ae44ca8f5892134386fddb/image:Llama3.1" }, "PretrainModelList": [ { "ID": "d40f4aff-e407-401a-a337-21bcdc4de8bb", "ModelName": "Meta-Llama-3-8B-Instruct" }, { "ID": "912934bb-a486-43aa-bd89-c285bf295347", "ModelName": "Yi-1.5-9B-Chat" }, { "ID": "34b35683-24a1-468e-a428-0b7f4196b2c2", "ModelName": "Meta-Llama-3.1-8B-Instruct" } ], "DatasetList": [], "BranchName": "master", "BootFile": "", "Parameters": [] }, { "Name": "部署Llama3.1", "Description": "", "JobType": "ONLINEINFERENCE", "Cluster": "C2Net", "ComputeSource": "GPU", "HasInternet": 2, "AccCardsNum": 1, "AccCardType": "A100", "CpuCores": 8, "MemGiB": 50, "GPUMemGiB": 40, "ShareMemGiB": 0, "Image": { "ImageID": "", "ImageName": "", "ImageUrl": "192.168.204.22:5000/default-workspace/99280a9940ae44ca8f5892134386fddb/image:Llama3.1" }, "PretrainModelList": [ { "ID": "34b35683-24a1-468e-a428-0b7f4196b2c2", "ModelName": "Meta-Llama-3.1-8B-Instruct" } ], "DatasetList": [], "BranchName": "master", "BootFile": "llama3.1_fastapi.py", "Parameters": [] }, { "Name": "部署Yi1.5", "Description": "", "JobType": "ONLINEINFERENCE", "Cluster": "C2Net", "ComputeSource": "GPU", "HasInternet": 2, "AccCardsNum": 1, "AccCardType": "A100", "CpuCores": 8, "MemGiB": 50, "GPUMemGiB": 40, "ShareMemGiB": 0, "Image": { "ImageID": "", "ImageName": "", "ImageUrl": "192.168.204.22:5000/default-workspace/99280a9940ae44ca8f5892134386fddb/image:Llama3" }, "PretrainModelList": [ { "ID": "912934bb-a486-43aa-bd89-c285bf295347", "ModelName": "Yi-1.5-9B-Chat" } ], "DatasetList": [], "BranchName": "master", "BootFile": "yi_fastapi.py", "Parameters": [] }, { "Name": "部署Llama3", "Description": "", "JobType": "ONLINEINFERENCE", "Cluster": "C2Net", "ComputeSource": "GPU", "HasInternet": 2, "AccCardsNum": 1, "AccCardType": "A100", "CpuCores": 8, "MemGiB": 50, "GPUMemGiB": 40, "ShareMemGiB": 0, "Image": { "ImageID": "", "ImageName": "", "ImageUrl": "192.168.204.22:5000/default-workspace/99280a9940ae44ca8f5892134386fddb/image:Llama3" }, "PretrainModelList": [ { "ID": "d40f4aff-e407-401a-a337-21bcdc4de8bb", "ModelName": "Meta-Llama-3-8B-Instruct" } ], "DatasetList": [], "BranchName": "master", "BootFile": "llama_fastapi.py", "Parameters": [] } ]openi_llm_example/README.md000066400000000000000000000053221466302463200160360ustar00rootroot00000000000000# 启智LLM在线体验任务脚本 > 此项目提供部署大模型推理服务的脚本，用于平台大模型在线体验。用户可根据此仓库的脚本与说明，使用云脑-在线推理部署对应的LLM API服务，用户自行部署的服务不包括对话前端界面。 ## 一. 如何体验大模型推理： 脚本将在启智云脑任务中启动FastAPI服务，大模型对话前端界面由平台提供。在启智AI协作平台上，在线体验的路径：**模型->大模型基地->自然语言处理模型->模型卡片->在线体验** ## 二. 在启智云脑的调试任务中测试LLM API服务： > 调试任务内部署服务仅可在环境内测试，无法对外部提供api服务。对外服务请参考下列的 三.在线推理 1. 使用本说明上方的 ***测试LLM API服务*** 云脑任务模板, 一键启动调试任务 2. 进入调试环境后，新建 notebook，运行下列代码，初始化调试环境，解压代码以及模型文件： ```python from c2net.context import prepare c2net_context = prepare() ``` 3. 刷新右侧文件导航栏，若能看见 openi_llm_example 文件夹，表示环境初始化成功。进入 openi_llm_example 目录并修改脚本（`llama_fastapi.py` 或 `yi_fastapi.py`） 将脚本最后一行代码中的服务端口，port参数，改为8000（8888端口将与jupyternotebook冲突）： ```python if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8000) ``` 4. 新建 Terminal 终端运行脚本，在调试环境内本地部署llm api服务： ```bash cd /tmp/code/openi_llm_example python llama_fastapi.py 或 python yi_fastapi.py ``` 5. 待服务完全启动后，再次新建一个 Terminal 终端，进入 openi_llm_example 目录，运行 `client.py` 脚本，若得到模型流式输出，则测试部署成功： ```bash cd /tmp/code/openi_llm_example python client.py ``` 示例输出： ```text prompt: 我的名字是什么 response: 从之前的对话中得知，你叫乔治 ``` ## 三. 使用启智云脑的在线推理任务部署LLM API服务： 1. 使用本说明上方的 ***部署llama3*** 或 ***部署Yi1.5*** 云脑任务模板, 一键启动在线推理，部署LLM API服务 2. 待任务状态变为 running 后，点击在线推理进入服务主页。此时页面出现502为正常现象，脚本中模型会先加载，待加载完成才会启动FastAPI应用服务。 3. 待服务完全启动后，刷新页面即可看到类似下图的信息： ![image](media/home.png) 4. 参考如上图所示的信息，使用 `curl`，`postman` 或执行本仓库中的 `client.py` 脚本等方式，向服务发送请求，即可得到模型的输出。 openi_llm_example/client.py000066400000000000000000000020661466302463200164110ustar00rootroot00000000000000import os import requests prompt = "我的名字是什么" print(f"prompt: {prompt}") payload = dict( message=prompt, history=[ { "user_message": "我叫乔治", "bot_response": "很高兴认识你，乔治！你来自哪里？有没有任何有趣的事情想和我分享？", } ], system_message="你是一个聊天助手，你需要用中文回答用户的问题!", max_tokens=4096, temperature=0.7, top_p=0.9, repetition_penalty=1.0, ) # 调试任务使用 base_url = os.getenv("OCTOPUS_NOTEBOOK_BASE_URL") url = f"http://127.0.0.1:8000{base_url}/chat" # 在线推理使用：任务启动后，复制地址到下列 url 参数中 # url = "" with requests.post( url=url, json=payload, stream=True, allow_redirects=True, ) as resp: if resp.status_code == 502: print("模型加载中，稍后再试。") if resp.status_code != 200: exit() print("response: ", end="") for text in resp.iter_content(decode_unicode=True): print(text, end="") openi_llm_example/llama3.1_fastapi.py000066400000000000000000000100151466302463200201430ustar00rootroot00000000000000""" 在线推理任务配置： 集群：智算GPU 资源：必须A100（V100不适配） 镜像：Llama3.1（chenzh创建） 模型：Meta-Llama-3.1-8B-Instruct (仓库 FoundationModel/meta-llama) 启动脚本: llama3.1_fastapi.py """ from c2net.context import prepare model_name = "Meta-Llama-3.1-8B-Instruct" c2net_context = prepare() model_path = c2net_context.pretrain_model_path + f"/{model_name}" import json from dataclasses import dataclass, field from datetime import datetime from threading import Thread from typing import Iterable, List, Optional, Tuple from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto") terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")] @dataclass class ChatHistory: user_message: str bot_response: str @dataclass class ChatParams: message: str history: Optional[List[ChatHistory]] = None system_message: str = "你是一个聊天助手，你需要用中文回答用户的问题!" max_tokens: int = 4096 temperature: float = 0.7 top_p: float = 0.9 repetition_penalty: float = 1.0 def streaming_chat(params: ChatParams) -> Iterable[str]: """ Generate a streaming response using the llama3.1-8b model. """ conversation = [{"role": "system", "content": params.system_message}] if params.history is not None: for _chat in params.history: conversation.extend( [ {"role": "user", "content": _chat.user_message}, {"role": "assistant", "content": _chat.bot_response}, ] ) conversation.append({"role": "user", "content": params.message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, do_sample=True, max_new_tokens=params.max_tokens, temperature=params.temperature, top_p=params.top_p, repetition_penalty=params.repetition_penalty, eos_token_id=terminators, ) # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash. if params.temperature == 0: generate_kwargs["do_sample"] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() for text in streamer: if text: yield text import os import uvicorn from fastapi import FastAPI from fastapi.responses import HTMLResponse, StreamingResponse app = FastAPI() base_url = os.getenv("OCTOPUS_NOTEBOOK_BASE_URL") sample_json = """ { "message":"我的名字是什么", "history":[ { "user_message":"我叫乔治", "bot_response":"很高兴认识你，乔治！你来自哪里？有没有任何有趣的事情想和我分享？" } ], "system_message":"你是一个聊天助手，你需要用中文回答用户的问题!", "max_tokens":512, "temperature":0.7, "top_p":0.9, "repetition_penalty":1.0 } """ desc = f"""

{model_name} 在线推理部署服务

请使用下方的🔗链接 与 🔎 JSON参数，发送 POST 请求进行推理。此API将返回流式数据。

🔗 POST https://mlunotebook.openi.org.cn{base_url}/chat

🔎 示例JSON参数：{sample_json}

""" @app.get(base_url) async def home(): return HTMLResponse(content=desc, status_code=200) @app.post(f"{base_url}/chat") async def streaming_chat_api(params: ChatParams): return StreamingResponse( streaming_chat(params), media_type="application/octet-stream; charset=utf-8", headers={"X-Accel-Buffering": "no"}, ) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8888)openi_llm_example/llama_comman_fastapi.py000066400000000000000000000105541466302463200212630ustar00rootroot00000000000000""" 在线推理任务配置： 集群：智算GPU 资源：必须A100（V100不适配） 镜像：Llama3（chenzh创建） 模型：Meta-Llama-3-8B-Instruct (仓库FoundationModel/llama3) 启动脚本: llama_fastapi.py """ from c2net.context import prepare import os c2net_context = prepare() files = [f for f in os.listdir(c2net_context.pretrain_model_path) if os.path.isdir(c2net_context.pretrain_model_path+ f"/{f}")] model_name = "" for file in files: if (not file.startswith(".") ) and len(os.listdir(c2net_context.pretrain_model_path+ f"/{file}")) > 0: model_name = file break print(f"当前模型：{model_name}") model_path = c2net_context.pretrain_model_path + f"/{model_name}" import json from dataclasses import dataclass, field from datetime import datetime from threading import Thread from typing import Iterable, List, Optional, Tuple from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer tokenizer = AutoTokenizer.from_pretrained(model_path,trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,device_map="auto") terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")] @dataclass class ChatHistory: user_message: str bot_response: str @dataclass class ChatParams: message: str history: Optional[List[ChatHistory]] = None system_message: str = "你是一个聊天助手，你需要用中文回答用户的问题!" max_tokens: int = 4096 temperature: float = 0.7 top_p: float = 0.9 repetition_penalty: float = 1.0 def streaming_chat(params: ChatParams) -> Iterable[str]: """ Generate a streaming response using the llama3-8b model. """ conversation = [{"role": "system", "content": params.system_message}] if params.history is not None: for _chat in params.history: conversation.extend( [ {"role": "user", "content": _chat.user_message}, {"role": "assistant", "content": _chat.bot_response}, ] ) conversation.append({"role": "user", "content": params.message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, do_sample=True, max_new_tokens=params.max_tokens, temperature=params.temperature, top_p=params.top_p, repetition_penalty=params.repetition_penalty, eos_token_id=terminators, ) # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash. if params.temperature == 0: generate_kwargs["do_sample"] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() for text in streamer: if text: yield text import os import uvicorn from fastapi import FastAPI from fastapi.responses import HTMLResponse, StreamingResponse app = FastAPI() base_url = os.getenv("OCTOPUS_NOTEBOOK_BASE_URL") sample_json = """ { "message":"我的名字是什么", "history":[ { "user_message":"我叫乔治", "bot_response":"很高兴认识你，乔治！你来自哪里？有没有任何有趣的事情想和我分享？" } ], "system_message":"你是一个聊天助手，你需要用中文回答用户的问题!", "max_tokens":512, "temperature":0.7, "top_p":0.9, "repetition_penalty":1.0 } """ desc = f"""

{model_name} 在线推理部署服务

请使用下方的🔗链接 与 🔎 JSON参数，发送 POST 请求进行推理。此API将返回流式数据。

🔗 POST https://mlunotebook.openi.org.cn{base_url}/chat

🔎 示例JSON参数：{sample_json}

""" @app.get(base_url) async def home(): return HTMLResponse(content=desc, status_code=200) @app.post(f"{base_url}/chat") async def streaming_chat_api(params: ChatParams): return StreamingResponse( streaming_chat(params), media_type="application/octet-stream; charset=utf-8", headers={"X-Accel-Buffering": "no"}, ) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8888) openi_llm_example/llama_fastapi.py000066400000000000000000000077761466302463200177450ustar00rootroot00000000000000""" 在线推理任务配置： 集群：智算GPU 资源：必须A100（V100不适配） 镜像：Llama3（chenzh创建） 模型：Meta-Llama-3-8B-Instruct (仓库FoundationModel/llama3) 启动脚本: llama_fastapi.py """ from c2net.context import prepare model_name = "Meta-Llama-3-8B-Instruct" c2net_context = prepare() model_path = c2net_context.pretrain_model_path + f"/{model_name}" import json from dataclasses import dataclass, field from datetime import datetime from threading import Thread from typing import Iterable, List, Optional, Tuple from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto") terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")] @dataclass class ChatHistory: user_message: str bot_response: str @dataclass class ChatParams: message: str history: Optional[List[ChatHistory]] = None system_message: str = "你是一个聊天助手，你需要用中文回答用户的问题!" max_tokens: int = 4096 temperature: float = 0.7 top_p: float = 0.9 repetition_penalty: float = 1.0 def streaming_chat(params: ChatParams) -> Iterable[str]: """ Generate a streaming response using the llama3-8b model. """ conversation = [{"role": "system", "content": params.system_message}] if params.history is not None: for _chat in params.history: conversation.extend( [ {"role": "user", "content": _chat.user_message}, {"role": "assistant", "content": _chat.bot_response}, ] ) conversation.append({"role": "user", "content": params.message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, do_sample=True, max_new_tokens=params.max_tokens, temperature=params.temperature, top_p=params.top_p, repetition_penalty=params.repetition_penalty, eos_token_id=terminators, ) # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash. if params.temperature == 0: generate_kwargs["do_sample"] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() for text in streamer: if text: yield text import os import uvicorn from fastapi import FastAPI from fastapi.responses import HTMLResponse, StreamingResponse app = FastAPI() base_url = os.getenv("OCTOPUS_NOTEBOOK_BASE_URL") sample_json = """ { "message":"我的名字是什么", "history":[ { "user_message":"我叫乔治", "bot_response":"很高兴认识你，乔治！你来自哪里？有没有任何有趣的事情想和我分享？" } ], "system_message":"你是一个聊天助手，你需要用中文回答用户的问题!", "max_tokens":512, "temperature":0.7, "top_p":0.9, "repetition_penalty":1.0 } """ desc = f"""

{model_name} 在线推理部署服务

请使用下方的🔗链接 与 🔎 JSON参数，发送 POST 请求进行推理。此API将返回流式数据。

🔗 POST https://mlunotebook.openi.org.cn{base_url}/chat

🔎 示例JSON参数：{sample_json}

""" @app.get(base_url) async def home(): return HTMLResponse(content=desc, status_code=200) @app.post(f"{base_url}/chat") async def streaming_chat_api(params: ChatParams): return StreamingResponse( streaming_chat(params), media_type="application/octet-stream; charset=utf-8", headers={"X-Accel-Buffering": "no"}, ) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8888) openi_llm_example/media/000077500000000000000000000000001466302463200156345ustar00rootroot00000000000000openi_llm_example/media/home.png000066400000000000000000002065131466302463200173010ustar00rootroot00000000000000PNG  IHDRuNsRGBgAMA a pHYsttfxIDATx^ ^Wy sOݚ55SSSSSskzFw*: C q 4A4L[Ȁh Jcinr"hAHLZV9\Myύ4kZ{UK{}<&p =9B =/BP_4 AAC{b'[Yz*_bznW4:*}AAz(+^A#/}){ ?h蹿FM /rAAAAAPl{Mys}.* 3M]~a܃    *6 sKK$:     hh>    .BAAAAcAAAAm]ƾkZz3Izֳ *k.z3!)AAAYձ|ݻc}~-Z잟9fGAP6`}AAATƽ*:Q?A<'),;ݪ[ AAA+ܡ!q+H;AS(?AAAъ2)tܳlAS0l^ ?  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>٘/^]^)]?hW6o_(=p[/τk%10ֆtˑ#${<:l Mᧆ9cs.n/#Ж/? /<H69<&;}\fm BF[kWG. N]o硳=e'pFX[5\kힸ<RX;,/xx~[7Y'8nI9=˾YOC~˶`;[Ǻy7ƸK*SrEbiIޅWfƯY#y\bmĶOe< V>嗢[_Swt":>bz.=WeKcrcML}VޒU|nȗb~dH$W1>hԔv=~f0s?{+>Vgr1`ÎC76]gD֨C ڢQHVM,tmhj2m^O1kNR}迲YGv9gHnzg} /IDAT_UdknGF2Np7#)46!JӖ8uޔAv)[oS>y_hpÀȭnˍWgjE}5bq|"C&?XcO5Zob!Ɛ^zҭ9#"LvlLQrAŴwqN|ݓEgC& {{=e]qhǧ\nmth"%k1{(b;Z>#{YS3^c?~,5ZKv|YyʧZn,x1P_J[ȿ-̗E׊fԵa͕^zrSn]ڠ56!>v07JqL[(>TZ6s`dc>grC~(#̔r{}{|^ #kɥkb8c6ߏw?>y{93g{~<ϝ Cjڤ3mY?낹O{_gmvz0ĕ~?rVq')k~b#L1t,?e>{&r3-cKc3):XB74n:kn`h&{uy`zCQэra_V%fyi1{O5~K.aYvgl߶iligikXz,1Ew}y\G8~+VWbz=0h|7}Ҋ3砯7t9Tv7 Z7+eΞkV]h}/E{*sl,6bэ$-}R:}=*C^w sd$Gط^_Y;"k6N̙q7H>ci _9̓>o[5vo3y.j|n.Jm+ίk*OC#FGa2v_:DrM:G4+l߲<Ie^_Ymqml5Ƴ V}9!z? ?-DǁmIew`orGuXlC37U+n 8n>W7kbڸjv^Ӛz(Iincǖ<֠E%չ&_R6͖顾jZZx*Xqkό˗n aTG8NKÆħ 5jۨyvcwlW}!5nNbOl=)-z[αաT'ygbC. $oKD>xmUbf~]~AGto|*(d.e[9ijsi"~PuI}c4V9.lگãqMfR/MZrxk N@R5M2Udch[;6H?t.4=!3}h2L.Eܤr1Ȫټe}M׵LykF#"ZݷN)d9='4#qO[R[yr}ׄLvx:StSkW5p_vh,u1pˌ'h]=7%)x 8\9p׳{ˡ{>8s7A{P@omn},PN_}]'|"_=.f{!_4pd.}j|Oa/ɴ?>f.&/} }]$&[>}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \.N}'>eI$pp8 \OҪ,ӗIENDB`openi_llm_example/pcmind_fastapi.py000066400000000000000000000106271466302463200201160ustar00rootroot00000000000000""" 在线推理任务配置： 集群：智算GPU 资源：A100、V100 镜像：Llama3（chenzh创建） 模型：PCMind-7B-1T-Transformers(仓库yanghanshuo/Online_inference) 启动脚本: pcmind_fastapi.py """ import os from c2net.context import prepare model_name = "PCMind-7B-1T-Transformers" c2net_context = prepare() model_path = c2net_context.pretrain_model_path + f"/{model_name}" import json import sys from dataclasses import dataclass, field from datetime import datetime from threading import Thread from typing import Iterable, List, Optional, Tuple from fastapi import FastAPI from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer sys.path.append(f"/tmp/pretrainmodel/{model_name}/") tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True) terminators = [tokenizer.eos_token_id] @dataclass class ChatHistory: user_message: str bot_response: str @dataclass class ChatParams: message: str history: Optional[List[ChatHistory]] = None system_message: str = "你是一个聊天助手，你需要用中文回答用户的问题!" max_tokens: int = 512 temperature: float = 0.7 top_p: float = 0.9 repetition_penalty: float = 1.0 def streaming_chat(params: ChatParams) -> Iterable[str]: """ Generate a streaming response using the llama3-8b model. """ conversation = [{"role": "system", "content": params.system_message}] if params.history is not None: for _chat in params.history: conversation.extend( [ {"role": "Human", "content": _chat.user_message}, {"role": "Assistant", "content": _chat.bot_response}, ] ) conversation.append({"role": "Human", "content": params.message}) input_str = "" for message_dict in conversation: input_str += message_dict["role"] + ": " + message_dict["content"] + "\n " """ input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_special_tokens=False).to( model.device ) """ input_ids = tokenizer.encode(input_str, return_tensors="pt", add_special_tokens=False).to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, do_sample=True, max_new_tokens=params.max_tokens, temperature=params.temperature, top_p=params.top_p, repetition_penalty=params.repetition_penalty, eos_token_id=terminators, ) # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash. if params.temperature == 0: generate_kwargs["do_sample"] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() for text in streamer: if text: yield text import os import uvicorn from fastapi import FastAPI from fastapi.responses import HTMLResponse, StreamingResponse app = FastAPI() base_url = os.getenv("OCTOPUS_NOTEBOOK_BASE_URL") sample_json = """ { "message":"我的名字是什么", "history":[ { "user_message":"我叫乔治", "bot_response":"很高兴认识你，乔治！你来自哪里？有没有任何有趣的事情想和我分享？" } ], "system_message":"你是一个聊天助手，你需要用中文回答用户的问题!", "max_tokens":512, "temperature":0.7, "top_p":0.9, "repetition_penalty":1.0 } """ desc = f"""

PengCheng-Mind 7B 在线推理部署服务

请使用下方的🔗链接 与 🔎 JSON参数，发送 POST 请求进行推理。此API将返回流式数据。

🔗 POST https://mlunotebook.openi.org.cn{base_url}/chat

🔎 示例JSON参数：{sample_json}

""" @app.get(base_url) async def home(): return HTMLResponse(content=desc, status_code=200) @app.post(f"{base_url}/chat") async def streaming_chat_api(params: ChatParams): return StreamingResponse( streaming_chat(params), media_type="application/octet-stream; charset=utf-8", headers={"X-Accel-Buffering": "no"}, ) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8888) openi_llm_example/yi_fastapi.py000066400000000000000000000100271466302463200172570ustar00rootroot00000000000000""" 在线推理任务配置： 集群：智算GPU 资源：A100（V100不适配） 镜像：Llama3（chenzh创建） 模型：Yi-1.5-9B-Chat (仓库FoundationModel/01-ai) 启动脚本: yi_fastapi.py """ from c2net.context import prepare model_name = "Yi-1.5-9B-Chat" c2net_context = prepare() model_path = c2net_context.pretrain_model_path + f"/{model_name}" import json from dataclasses import asdict, dataclass, field from datetime import datetime from threading import Thread from typing import Iterable, List, Optional, Tuple from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto") terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")] @dataclass class ChatHistory: user_message: str bot_response: str @dataclass class ChatParams: message: str history: Optional[List[ChatHistory]] = None system_message: str = "你是一个聊天助手，你需要用中文回答用户的问题!" max_tokens: int = 512 temperature: float = 0.7 top_p: float = 0.9 repetition_penalty: float = 1.0 def streaming_chat(params: ChatParams) -> Iterable[str]: """ Generate a streaming response """ conversation = [{"role": "system", "content": params.system_message}] if params.history is not None: for _chat in params.history: conversation.extend( [ {"role": "user", "content": _chat.user_message}, {"role": "assistant", "content": _chat.bot_response}, ] ) conversation.append({"role": "user", "content": params.message}) input_ids = tokenizer.apply_chat_template( conversation, add_generation_prompt=True, tokenize=True, return_tensors="pt" ).to(next(model.parameters()).device) streamer = TextIteratorStreamer(tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, do_sample=True, max_new_tokens=params.max_tokens, temperature=params.temperature, top_p=params.top_p, repetition_penalty=params.repetition_penalty, eos_token_id=terminators, ) # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash. if params.temperature == 0: generate_kwargs["do_sample"] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() for text in streamer: if text: yield text import os import uvicorn from fastapi import FastAPI from fastapi.responses import HTMLResponse, StreamingResponse app = FastAPI() base_url = os.getenv("OCTOPUS_NOTEBOOK_BASE_URL") sample_json = """ { "message":"我的名字是什么", "history":[ { "user_message":"我叫乔治", "bot_response":"很高兴认识你，乔治！你来自哪里？有没有任何有趣的事情想和我分享？" } ], "system_message":"你是一个聊天助手，你需要用中文回答用户的问题!", "max_tokens":512, "temperature":0.7, "top_p":0.9, "repetition_penalty":1.0 } """ desc = f"""

{model_name} 在线推理部署服务

请使用下方的🔗链接 与 🔎 JSON参数，发送 POST 请求进行推理。此API将返回流式数据。

🔗 POST https://mlunotebook.openi.org.cn{base_url}/chat

🔎 示例JSON参数：{sample_json}

""" @app.get(base_url) async def home(): return HTMLResponse(content=desc, status_code=200) @app.post(f"{base_url}/chat") async def streaming_chat_api(params: ChatParams): return StreamingResponse( streaming_chat(params), media_type="application/octet-stream; charset=utf-8", headers={"X-Accel-Buffering": "no"}, ) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8888)