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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'lmsys/vicuna-13b-v1.5-16k',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="lmsys/vicuna-13b-v1.5-16k",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
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Product Detail
Discover the Vicuna v1.5 16K (13B), a cutting-edge open-source large language model (LLM) meticulously developed by the LMSYS Org. Launched in May 2023, this model is an advanced iteration of the original Vicuna, engineered to deliver unparalleled conversational AI capabilities and proficiently handle a diverse range of natural language processing tasks.
🚀 Key Information at a Glance
- Model Name: Vicuna v1.5 16K (13B)
- Developer: LMSYS Org
- Release Date: May 2023
- Version: 1.5
- Model Type: Large Language Model (LLM)
🌟 Core Capabilities and Features
- ✓ Extended Context Length: Features an impressive 16K context window, achieved through linear RoPE scaling, allowing for deeper understanding and generation of longer texts and complex conversations.
- ✓ Enhanced Performance: Delivers superior performance compared to its predecessor, offering more accurate, relevant, and coherent outputs across various tasks.
- ✓ Open-Source Accessibility: Freely available for research and development, fostering collaboration and innovation within the global AI community.
- ✓ Broad Task Handling: Adept at managing a wide array of language tasks, including text generation, summarization, question-answering, and sophisticated language understanding.
- ✓ Diverse Training Data: Benefitted from training on an extensive and varied dataset of web content, contributing to its robust general knowledge and adaptability.
🎯 Intended Use & Language Support
The Vicuna v1.5 16K (13B) is primarily targeted for academic research, advanced chatbot applications, and various natural language processing (NLP) tasks. This includes intricate text generation, precise question-answering, and deep language comprehension.
Its primary operational language is English, with potential capabilities in other languages stemming from the diversity of its expansive training dataset.
⚙️ Technical Architecture & Training
Architecture:
Vicuna v1.5 16K (13B) is fundamentally based on the powerful LLaMA architecture. It features a decoder-only, transformer-based model equipped with 13 billion parameters, ensuring efficient and robust processing of large volumes of textual data.
Training Data & Diversity:
The model underwent training on a highly diverse dataset encompassing a broad spectrum of web content, including:
- ShareGPT conversations
- Extensive collections of books
- Academic papers and scholarly articles
- Comprehensive code repositories
- General web pages and forums
Data Source and Size:
While the precise scale of the training data is not explicitly quantified, it is estimated to span from hundreds of gigabytes to several terabytes, a testament to the model's vast knowledge base and capabilities.
Knowledge Cutoff:
The exact knowledge cutoff date for Vicuna v1.5 16K (13B) is not officially disclosed. However, aligning with its May 2023 release, its comprehensive knowledge base is likely current up to early 2023.
📊 Performance Insights & Responsible Usage
Accuracy:
Vicuna v1.5 16K (13B) showcases significant performance improvements over previous versions. While specific benchmark figures are not provided, it has consistently achieved competitive results in various evaluations, reflecting its high accuracy and generation quality.
Speed:
The inference speed of Vicuna v1.5 16K (13B) is primarily contingent on the hardware infrastructure used for deployment. As a 13-billion-parameter model, it requires substantial computational resources to operate efficiently in real-time applications.
Robustness:
This model is engineered for broad applicability across various language tasks and thematic domains. Its performance can naturally vary based on the specific context and the diversity of its training data.
📚 Usage & Code Samples:
While specific code examples for API integration are typically found in detailed developer documentation, Vicuna v1.5 16K (13B) supports standard interfaces for tasks such as chat completion. Developers can generally refer to the official lmsys/vicuna-13b-v1.5-16k repositories for implementation guidance.
⚖️ Ethical Guidelines & Bias Awareness:
Users are strongly encouraged to exercise caution and awareness regarding potential biases in the model's outputs, which may stem from its training data. Implementing robust content filtering, continuous monitoring, and safety measures is crucial for responsible deployment in any production environment.
License Type:
Vicuna v1.5 16K (13B) is released under an open-source license, making it freely available for research, development, and non-commercial projects. Users should consult the specific license terms for any commercial applications.
❓ Frequently Asked Questions (FAQ)
Q1: What defines Vicuna v1.5 16K (13B)?
A1: It's an open-source large language model by LMSYS Org, released in May 2023. It's an enhanced version of the original Vicuna, boasting a 16K context length for advanced conversational AI and NLP tasks.
Q2: What key advancements does Vicuna v1.5 16K (13B) offer?
A2: Significant advancements include an extended 16K context window via linear RoPE scaling, substantial performance improvements over its predecessor, and its continued status as a freely available open-source model.
Q3: Can Vicuna v1.5 16K (13B) be utilized for commercial projects?
A3: It is released under an open-source license, primarily intended for research and development. While integration into applications is possible, users must meticulously review its specific license terms to ensure compliance for commercial deployment and implement necessary safety protocols.
Q4: What types of data contributed to the training of Vicuna v1.5 16K (13B)?
A4: The model was trained on a comprehensive and diverse collection of web content, including ShareGPT conversations, books, academic papers, code repositories, and general web pages, providing it with a broad knowledge foundation.
Q5: How can users mitigate potential biases in the model's outputs?
A5: Users should be proactive in acknowledging that, like all LLMs, this model may exhibit biases present in its training data. Implementing robust content filtering, continuous monitoring, and safety measures during deployment is crucial for mitigating and addressing any biased outputs, ensuring ethical use.
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