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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: 'allenai/OLMo-7B-Twin-2T',
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="allenai/OLMo-7B-Twin-2T",
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}")
-
AI Playground
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Product Detail
✨ OLMO TWIN-2T (7B) Overview: A Transparent Open-Source LLM
The OLMO TWIN-2T (7B) is a cutting-edge, open-source large language model (LLM) developed by the Allen Institute for Artificial Intelligence in collaboration with leading universities including the University of Washington, Yale, New York University, and Carnegie Mellon. Designed for maximum transparency, this 7-billion parameter model empowers the NLP research community by offering unparalleled insight into its training processes, data diversity, architectural choices, and performance metrics.
It stands as a crucial tool for both academic and commercial applications, particularly for those focused on studying and enhancing the bias, fairness, and robustness of language models. Its open approach fosters innovation and responsible AI development.
💡 Key Information
- Model Name: OLMO TWIN-2T (7B)
- Developer: Allen Institute for Artificial Intelligence & collaborators
- Release Date: Inferred post-2023
- Version: 7 Billion Parameters
- Model Type: Text-based Large Language Model (Transformer Architecture)
✅ Distinctive Features & Intended Use
- Open-source Frameworks: Access to comprehensive training and evaluation tools.
- High Transparency: Unrivaled visibility into training data, processes, and performance.
- Broad Application Support: Facilitates diverse NLP tasks through extensive tuning and adaptations.
- Intermediate Checkpoints: Provides access to crucial training logs and model checkpoints.
Intended Use: The OLMO TWIN-2T (7B) is ideal for academic research, especially in areas of bias, fairness, and robustness in LLMs. It's also perfectly suited for developers requiring highly transparent and adaptable NLP capabilities for their applications. While specific language capabilities are not detailed, its training dataset suggests multilingual support.
⚙️ Technical Deep Dive
- Architecture: Built on a decoder-only transformer architecture, drawing improvements from models like PaLM and LLaMA. It incorporates innovative features such as non-parametric layer norms and SwiGLU activation functions to enhance stability and performance.
- Training Data: Trained on the extensive 'Dolma' dataset. This comprehensive corpus comprises trillions of tokens from diverse sources including web pages, social media, and scholarly articles, ensuring broad linguistic coverage and mitigating potential biases.
- Knowledge Cutoff: The model incorporates knowledge and studies up to and including 2024.
- Diversity & Bias: Rigorous evaluations of data diversity are a core part of its training regimen, with built-in checks designed to foster a more balanced and fair model. The inherent diversity of the Dolma dataset is fundamental to achieving this goal.
🚀 Performance Benchmarks
- Comparative Performance: Demonstrates competitive and often superior results against established models like LLaMA and Falcon across various NLP benchmarks.
- Accuracy: Exhibits strong accuracy across a wide spectrum of NLP tasks, including impressive zero-shot capabilities.
- Speed & Robustness: Engineered for high throughput and exceptional stability, validated through comprehensive speed tests and robustness evaluations under diverse input conditions.
⚖️ Ethical Considerations & Licensing
The development team behind OLMO TWIN-2T (7B) places a strong emphasis on ethical AI guidelines and responsible use. They adhere to published standards and best practices, ensuring the model's deployment contributes positively to the AI landscape.
Licensing: The model is freely available under the Apache 2.0 License, supporting both commercial and non-commercial applications. All associated materials and tools are accessible at no cost, promoting widespread adoption and further research.
❓ Frequently Asked Questions (FAQ)
A1: Its open-source nature provides complete transparency into its training, data, and architecture, making it an invaluable tool for NLP researchers to study and improve language models, especially concerning bias and fairness.
A2: It was developed by the Allen Institute for Artificial Intelligence (AI2) in collaboration with several prominent universities, including the University of Washington, Yale, NYU, and Carnegie Mellon.
A3: The model was trained on the 'Dolma' dataset, a comprehensive and diverse corpus containing trillions of tokens sourced from web pages, social media, scholarly articles, and more.
A4: Yes, it is released under the Apache 2.0 License, which permits both commercial and non-commercial applications at no cost.
A5: OLMO TWIN-2T (7B) demonstrates competitive, and often superior, performance compared to models like LLaMA and Falcon across various NLP benchmarks, including strong accuracy and zero-shot capabilities.
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