Instructions to use cloudyu/60B_MoE_Coder_v3_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cloudyu/60B_MoE_Coder_v3_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cloudyu/60B_MoE_Coder_v3_gguf", filename="cloudyu_60B_MoE_Coder_v3.Q6_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cloudyu/60B_MoE_Coder_v3_gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K # Run inference directly in the terminal: llama-cli -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K # Run inference directly in the terminal: llama-cli -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K # Run inference directly in the terminal: ./llama-cli -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
Use Docker
docker model run hf.co/cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
- LM Studio
- Jan
- Ollama
How to use cloudyu/60B_MoE_Coder_v3_gguf with Ollama:
ollama run hf.co/cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
- Unsloth Studio new
How to use cloudyu/60B_MoE_Coder_v3_gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cloudyu/60B_MoE_Coder_v3_gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cloudyu/60B_MoE_Coder_v3_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cloudyu/60B_MoE_Coder_v3_gguf to start chatting
- Docker Model Runner
How to use cloudyu/60B_MoE_Coder_v3_gguf with Docker Model Runner:
docker model run hf.co/cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
- Lemonade
How to use cloudyu/60B_MoE_Coder_v3_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cloudyu/60B_MoE_Coder_v3_gguf:Q6_K
Run and chat with the model
lemonade run user.60B_MoE_Coder_v3_gguf-Q6_K
List all available models
lemonade list
Create README.md
Browse files
README.md
ADDED
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example output
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写一个python程序抓取youtube最流行的内容:从网页获取数据,
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以下是一个python脚本,可以从Youtube上抓取最受欢迎的视频并将其保存到本地文件中。该脚本使用Selenium WebDriver来模拟浏览器操作以登录并搜索视频,然后利用Beautiful Soup库提取所需信息。如果您需要任何帮助或修改建议,请随时告诉我!
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```
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from selenium import webdriver
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from bs4 import BeautifulSoup as BS
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import os
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# 设置ChromeDriver路径
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CHROMEDRIVER_PATH = './chromedriver.exe' # 更换为实际路径
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def main():
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# 启动Chrome浏览器并登录Youtube
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browser = webdriver.Chrome(executable_path=CHROMEDRIVER_PATH)
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browser.get('https://www.youtube.com/')
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login_url = 'https://accounts.google.com/signin'
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browser.get(login_url)
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# 模拟输入账号和密码并登录
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username = input('Username: ')
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password = getpass.getpass() # 防止密码显示在终端上
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submit_button = browser.find_element_by_class_name('whsLW', timeout=5)
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submit_button.click()
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# 在搜索栏中输入关键词并查找结果
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search_query = 'most popular youtube videos today'
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search_box = browser.find_element_by_id('search_query')
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search_box.send_keys(search_query)
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enter_key = browser.find_element_by_class_name('ytp-suggest-renderer__input-icon')
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enter_key.click()
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results = BS(browser.page_source, 'html.parser')
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# 从搜索结果中提取出最受欢迎的视频链接并保存到本地文件
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videos = results.find('ol', {'class': 'r'})
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video_urls = [a['href'] for a in videos.find_all('a')]
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saved_path = 'popular_videos/' + os.getcwd().split('/')[-2] + '_' + str(int(time.time())) + '/'
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if not os.path.exists(saved_path):
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os.makedirs(saved_path)
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for i, url in enumerate(video_urls[:50]):
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filename = saved_path + '%d.mp4' % (i+1)
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urllib.request.urlretrieve(url, filename)
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print('Downloaded top 50 videos to:', saved_path)
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except Exception as e:
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print(e) [end of text]
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```
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