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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: 'OpenAssistant/stablelm-7b-sft-v7-epoch-3',
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="OpenAssistant/stablelm-7b-sft-v7-epoch-3",
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
ℹ Open-Assistant StableLM SFT-7 (7B): Model Overview
The Open-Assistant StableLM SFT-7 (7B) is a cutting-edge open-source Large Language Model (LLM) developed by Open-Assistant and released in April 2023 (Version 1.0). Built upon the robust StableLM architecture, this model has undergone meticulous Supervised Fine-Tuning (SFT) to enhance its capabilities across a diverse range of natural language processing tasks.
It is specifically engineered to empower developers and researchers, offering an accessible platform for generating highly human-like text responses and performing complex linguistic operations.
✓ Essential Capabilities & Features
- ✓ 7 Billion Parameters: A substantial model size enabling sophisticated language understanding and generation.
- ✓ Open-Source & Freely Available: Ensuring broad accessibility and fostering community-driven innovation.
- ✓ Supervised Fine-Tuning (SFT): Leverages advanced fine-tuning techniques for optimized performance.
- ✓ High-Quality Text Generation: Capable of producing coherent, contextually relevant, and human-like text responses.
- ✓ Multilingual Support: Designed to process and generate text in multiple languages, with a primary focus on English and other widely spoken languages.
● Versatile Applications
This highly adaptable model is suitable for a wide array of Natural Language Processing (NLP) tasks, including:
- ● Advanced Text Generation and Content Creation
- ● Sophisticated Question Answering Systems
- ● Efficient Text Summarization
- ● Accurate Language Translation
- ● Code Generation and Analysis for Developers
ℹ Technical Specifications
Architecture
The Open-Assistant StableLM SFT-7 (7B) is built upon the widely-adopted transformer architecture, a cornerstone for modern large language models. It is highly probable that it utilizes a decoder-only transformer design, akin to other leading generative models like those in the GPT series.
Training Data & Knowledge Cutoff
While precise details regarding the training dataset are not publicly disclosed, as an open-source project from LAION and Stability AI, it is expected to have been trained on a massive and diverse collection of publicly available text data. This typically includes vast amounts of web-crawled text, books, and other digital content, potentially spanning hundreds of gigabytes to several terabytes.
The exact knowledge cutoff date is not explicitly stated. However, given its release in April 2023, it is reasonable to assume that its knowledge base reflects information available up to sometime in late 2022 or early 2023.
Diversity and Bias
Without specific information on the training data's composition, a thorough evaluation of the model's diversity and potential biases remains challenging. Nonetheless, open-source projects typically prioritize efforts to address and mitigate biases, and users are encouraged to conduct their own assessments.
Performance Metrics & Considerations
Detailed performance metrics for the StableLM SFT-7 (7B) model are not provided in the available information. However, typical evaluation metrics for language models of this scale often include:
- ✓ Perplexity: A key indicator of how well the model predicts a sample of text; lower values signify better performance.
- ✓ BLEU Score: Primarily used for assessing the quality of machine translation outputs.
- ✓ ROUGE Score: Employed to evaluate the quality and accuracy of text summarization tasks.
- ✓ F1 Score: A common metric for evaluating the accuracy of classification tasks.
Inference Speed & Robustness
The inference speed for a 7 billion parameter model varies considerably based on the hardware utilized. On modern GPUs, generating responses typically ranges from milliseconds to a few seconds, depending on the length and complexity of the output.
The model's robustness across diverse topics and languages is directly influenced by the richness and variety of its training data. A 7-billion parameter model is expected to possess strong generalization capabilities, though specific performance across highly varied inputs warrants further rigorous testing and evaluation.
⚠ Usage & Ethical Guidelines
Accessing the Model
While specific usage instructions for the Open-Assistant StableLM SFT-7 (7B) were not detailed in the provided information, as an open-source model, it is typically accessible and integrated through widely used machine learning frameworks such as PyTorch or TensorFlow. Developers should consult the official Open-Assistant project repository for definitive documentation and code examples.
(Code samples or integration snippets, such as those referencing "open-ai.chat-completion" or "OpenAssistant/stablelm-7b-sft-v7-epoch-3", would typically be found here in the official documentation.)
Ethical AI Principles
It is paramount for all users to adhere to established AI ethics principles when interacting with or deploying large language models. Key ethical considerations include:
- ⚠ Avoiding Harmful Content: Proactively preventing the generation, promotion, or dissemination of biased, discriminatory, or otherwise offensive content.
- ⚠ Respecting Intellectual Property: Ensuring compliance with copyright laws and respecting all forms of intellectual property rights.
- ⚠ Promoting Transparency: Clearly indicating when content has been generated or augmented by AI.
- ⚠ Protecting User Privacy: Implementing robust measures to safeguard personal data and ensure user privacy during any data processing.
License Information
The specific license governing the Open-Assistant StableLM SFT-7 (7B) model was not explicitly mentioned in the available details. However, as a public open-source project, it is typically released under a permissive open-source license such as MIT, Apache 2.0, or Creative Commons, which generally allow for wide usage, modification, and distribution. Users are advised to check the official project's repository or documentation for the definitive licensing terms.
❓ Frequently Asked Questions (FAQs)
Q1: What is the Open-Assistant StableLM SFT-7 (7B)?
A1: It's a 7-billion parameter open-source Large Language Model (LLM) released by Open-Assistant in April 2023. It's built on the StableLM architecture and uses Supervised Fine-Tuning (SFT) for various NLP tasks.
Q2: What are the primary uses for this model?
A2: The model is designed for a broad range of NLP applications including text generation, question answering, summarization, language translation, and code generation and analysis.
Q3: Is Open-Assistant StableLM SFT-7 (7B) truly open-source?
A3: Yes, it is an open-source model developed by Open-Assistant and is freely available. While the specific license details weren't provided, it's expected to be under a permissive open-source license like MIT or Apache 2.0.
Q4: What is the knowledge cutoff date for this model?
A4: The exact knowledge cutoff date is not specified. However, given its release in April 2023, its training data likely extends up to late 2022 or early 2023.
Q5: How can developers access and integrate the StableLM SFT-7 (7B) model?
A5: As an open-source model, it can typically be accessed and integrated via popular machine learning frameworks such as PyTorch or TensorFlow. Developers should consult the official Open-Assistant project repository for detailed documentation, code samples, and integration guides.
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