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SambaStudio

SambaNova 的 Sambastudio 是一個平台，可讓您訓練、執行批次推論工作，以及部署線上推論端點，以執行您自行微調的開放原始碼模型。

注意

您目前正在瀏覽的文件頁面說明如何將 SambaStudio 模型用作文字完成模型。我們建議您使用聊天完成模型。

您可能正在尋找 SambaStudio 聊天模型。

概觀​

整合詳細資訊​

類別套件本地可序列化JS 支援套件下載套件最新版
SambaStudiolangchain_community❌beta❌PyPI - DownloadsPyPI - Version

此範例說明如何使用 LangChain 與 SambaStudio 模型互動

設定​

憑證​

需要 SambaStudio 環境才能部署模型。請至 sambanova.ai/products/enterprise-ai-platform-sambanova-suite 取得更多資訊

您需要部署端點並設定 SAMBASTUDIO_URL 和 SAMBASTUDIO_API_KEY 環境變數

import getpass
import os

if "SAMBASTUDIO_URL" not in os.environ:
    os.environ["SAMBASTUDIO_URL"] = getpass.getpass()
if "SAMBASTUDIO_API_KEY" not in os.environ:
    os.environ["SAMBASTUDIO_API_KEY"] = getpass.getpass()

安裝​

此整合位於 langchain-community 套件中。我們還需要安裝 sseclient-py 套件，這是執行串流預測所必需的

%pip install --quiet -U langchain-community sseclient-py

例項化​

from langchain_community.llms.sambanova import SambaStudio

llm = SambaStudio(
    model_kwargs={
        "do_sample": True,
        "max_tokens": 1024,
        "temperature": 0.01,
        "process_prompt": True,  # set if using CoE endpoints
        "model": "Meta-Llama-3-70B-Instruct-4096",  # set if using CoE endpoints
        # "repetition_penalty":  1.0,
        # "top_k": 50,
        # "top_logprobs": 0,
        # "top_p": 1.0
    },
)
API 參考：SambaStudio

調用​

現在我們可以例項化我們的模型物件並產生聊天完成

input_text = "Why should I use open source models?"

completion = llm.invoke(input_text)
completion
"Using open source models can have numerous benefits. Here are some reasons why you should consider using open source models:\n\n1. **Cost-effective**: Open source models are often free to use, modify, and distribute, which can significantly reduce costs compared to proprietary models.\n2. **Customizability**: Open source models can be modified to fit your specific needs, allowing you to tailor the model to your project's requirements.\n3. **Transparency**: Open source models provide complete transparency into the model's architecture, training data, and algorithms, which can be essential for understanding how the model works and identifying potential biases.\n4. **Community involvement**: Open source models are often maintained by a community of developers, researchers, and users, which can lead to faster bug fixes, new feature additions, and improved performance.\n5. **Flexibility**: Open source models can be used in a variety of applications, from research to production, and can be easily integrated into different workflows and systems.\n6. **Auditability**: With open source models, you can audit the model's performance, data, and algorithms, which is critical in regulated industries or when working with sensitive data.\n7. **No vendor lock-in**: By using open source models, you're not tied to a specific vendor or proprietary technology, giving you more freedom to switch or modify your approach as needed.\n8. **Improved security**: Open source models can be reviewed and audited by the community, which can help identify and fix security vulnerabilities more quickly.\n9. **Access to cutting-edge research**: Open source models can provide access to the latest research and advancements in AI and machine learning, allowing you to leverage the work of experts in the field.\n10. **Ethical considerations**: By using open source models, you can ensure that your AI systems are transparent, explainable, and fair, which is essential for building trust in AI applications.\n11. **Reduced risk of bias**: Open source models can help reduce the risk of bias by providing transparency into the model's development, training data, and algorithms.\n12. **Faster development**: Open source models can accelerate your development process by providing pre-trained models, datasets, and tools that can be easily integrated into your project.\n13. **Improved collaboration**: Open source models can facilitate collaboration among researchers, developers, and organizations, leading to faster progress and innovation in AI and machine learning.\n14. **Access to large datasets**: Open source models can provide access to large datasets, which can be essential for training and testing AI models.\n15. **Compliance with regulations**: In some cases, using open source models can help ensure compliance with regulations, such as GDPR, HIPAA, or CCPA, which require transparency and explainability in AI systems.\n\nOverall, using open source models can provide numerous benefits, from cost savings to improved transparency and customizability. By leveraging open source models, you can accelerate your AI and machine learning projects while ensuring that your systems are transparent, explainable, and fair."
# Streaming response
for chunk in llm.stream("Why should I use open source models?"):
    print(chunk, end="", flush=True)
Using open source models can have numerous benefits. Here are some reasons why you should consider using open source models:

1. **Cost-effective**: Open source models are often free to use, modify, and distribute, which can significantly reduce costs compared to proprietary models.
2. **Customizability**: Open source models can be modified to fit your specific needs, allowing you to tailor the model to your project's requirements.
3. **Transparency**: Open source models provide complete transparency into the model's architecture, training data, and algorithms, which can be essential for understanding how the model works and identifying potential biases.
4. **Community involvement**: Open source models are often maintained by a community of developers, researchers, and users, which can lead to faster bug fixes, new feature additions, and improved performance.
5. **Flexibility**: Open source models can be used in a variety of applications, from research to production, and can be easily integrated into different workflows and systems.
6. **Auditability**: With open source models, you can audit the model's performance, data, and algorithms, which is critical in regulated industries or when working with sensitive data.
7. **No vendor lock-in**: By using open source models, you're not tied to a specific vendor or proprietary technology, giving you more freedom to switch or modify your approach as needed.
8. **Improved security**: Open source models can be reviewed and audited by the community, which can help identify and fix security vulnerabilities more quickly.
9. **Access to cutting-edge research**: Open source models can provide access to the latest research and advancements in AI and machine learning, allowing you to leverage the work of experts in the field.
10. **Ethical considerations**: By using open source models, you can ensure that your AI systems are transparent, explainable, and fair, which is essential for building trust in AI applications.
11. **Reduced risk of bias**: Open source models can help reduce the risk of bias by providing transparency into the model's development, training data, and algorithms.
12. **Faster development**: Open source models can accelerate your development process by providing pre-trained models, datasets, and tools that can be easily integrated into your project.
13. **Improved collaboration**: Open source models can facilitate collaboration among researchers, developers, and organizations, leading to faster progress and innovation in AI and machine learning.
14. **Access to large datasets**: Open source models can provide access to large datasets, which can be essential for training and testing AI models.
15. **Compliance with regulations**: In some cases, using open source models can help ensure compliance with regulations, such as GDPR, HIPAA, or CCPA, which require transparency and explainability in AI systems.

Overall, using open source models can provide numerous benefits, from cost savings to improved transparency and customizability. By leveraging open source models, you can accelerate your AI and machine learning projects while ensuring that your systems are transparent, explainable, and fair.

鏈結​

from langchain_core.prompts import PromptTemplate

prompt = PromptTemplate.from_template("How to say {input} in {output_language}:\n")

chain = prompt | llm
chain.invoke(
    {
        "output_language": "German",
        "input": "I love programming.",
    }
)
API 參考：PromptTemplate
'In German, you can say:\n\n"Ich liebe das Programmieren."\n\nHere\'s a breakdown of the sentence:\n\n* "Ich" means "I"\n* "liebe" is the verb "to love" in the present tense, first person singular (I love)\n* "das" is the definite article "the"\n* "Programmieren" is the noun "programming"\n\nSo, "Ich liebe das Programmieren" literally means "I love the programming".\n\nIf you want to make it sound more casual, you can say:\n\n"Ich liebe\'s Programmieren."\n\nThe apostrophe in "liebe\'s" is a contraction of "liebe es", which is a more informal way of saying "I love it".\n\nAlternatively, you can also say:\n\n"Programmieren ist meine Leidenschaft."\n\nThis sentence means "Programming is my passion". Here, "Programmieren" is the subject, "ist" is the verb "to be" in the present tense, and "meine Leidenschaft" means "my passion".'

API 參考​

如需所有 SambaStudio llm 功能和設定的詳細文件，請前往 API 參考：https://langchain-python.dev.org.tw/api_reference/community/llms/langchain_community.llms.sambanova.SambaStudio.html

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