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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: 'mistralai/mistral-tiny',
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="mistralai/mistral-tiny",
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
Introducing Mistral Tiny: Your Lightweight Language Model for Efficient AI
Mistral Tiny, developed by Mistral AI and officially released in October 2024 (Version 1.0), is a cutting-edge, lightweight language model engineered for remarkable efficiency in various text-based tasks. This text-type model is specifically optimized to operate effectively in resource-constrained environments, ensuring high performance even with limited computational resources.
Basic Information:
- ✨ Model Name: Mistral Tiny
- 👩💻 Developer/Creator: Mistral AI
- 🗓️ Release Date: October 2024
- 🔄 Version: 1.0
- 📝 Model Type: Text
Key Capabilities & Specifications
Core Features:
- 📏 Model Size: A compact 106.6 million parameters.
- 💾 Required VRAM: Only 0.4 GB, making it incredibly accessible for devices with limited resources.
- 📖 Context Length: Supports an extensive maximum context length of 131,072 tokens, enabling comprehensive context handling.
- ⚙️ Tokenizer Class: Utilizes the LlamaTokenizer with a vocabulary size of 32,000 tokens.
- 🛠️ Training Framework: Built on the MistralForCausalLM architecture, compatible with Transformers version 4.39.1.
Intended Applications:
Mistral Tiny is perfectly suited for applications demanding rapid responses and low-latency processing, making it ideal for:
- Chatbots
- Automated content generation
- Educational tools
- Efficient text summarization
- Reliable code completion tasks
Multilingual Support:
The model offers robust language support, including English, French, German, Spanish, and Italian, broadening its global applicability.
Technical Architecture & Training
Architecture Overview:
Mistral Tiny employs a sophisticated Transformer architecture, designed for optimal performance:
- 🧱 Layers: 12 layers
- 🧠 Attention Heads: 12 attention heads per layer
- 📏 Hidden Size: 768 dimensions
- ↔️ Intermediate Size: 3072 dimensions
This architecture integrates advanced attention techniques like Sliding Window Attention (SWA) to efficiently manage long sequences and maintain contextual coherence.
Training Data & Knowledge Cutoff:
The model was rigorously trained on a diverse dataset comprising over 7 trillion tokens from various domains. This extensive training corpus ensures robust language understanding and contextual awareness. The knowledge cutoff for Mistral Tiny is September 2023.
Diversity and Bias Mitigation:
Mistral AI has prioritized creating a diverse training dataset to actively mitigate biases related to gender, race, and ideology. The model's design focuses on enhancing its applicability across a broad spectrum of contexts and topics, promoting fairness and inclusivity.
Performance Benchmarks:
- 🎯 Accuracy: Achieves an accuracy rate exceeding 85% in language understanding tasks.
- 📉 Perplexity Score: Demonstrates a low perplexity score, indicative of strong predictive capabilities and high confidence in generating natural language.
- 🏆 F1 Score: Maintains an F1 score above 0.75 in text classification tasks.
Benchmarking Results:
- 📈 MMLU (Massive Multitask Language Understanding): Exhibits high performance in diverse language comprehension tasks.
- 💻 HumanEval Benchmark (for coding): Secures competitive rankings among models of similar sizes, showcasing its capability in code generation and understanding.
Mistral Tiny vs. Other Mistral Models
Mistral Tiny stands out as a compact, efficient language model, specifically engineered for speed and cost-effectiveness in straightforward applications. With over 85% accuracy on simple tasks, it offers exceptional value for direct use cases.
- ➡️ Mistral Small: This model is suitable for bulk tasks with moderate latency, achieving 72.2% accuracy on benchmarks, balancing performance with resource utilization.
- ➡️ Mistral Large: Excels in complex tasks, offering advanced reasoning capabilities and comprehensive multilingual support with 84.0% accuracy, designed for highly demanding scenarios.
- ➡️ For exceptionally demanding applications requiring superior coding and complex reasoning, consider Mixtral 8x7B, which provides up to 6x faster inference.
(Referenced from: Mixtral 8x7B Instruct v0.1)
How to Use Mistral Tiny
Code Samples & API Access:
Mistral Tiny is readily available on the AI/ML API platform under the identifier "mistralai/mistral-tiny". This seamless integration allows developers to quickly incorporate Mistral Tiny into their projects.
For detailed implementation guidance, comprehensive code examples, and API endpoints, refer to the exhaustive AI.cc API Documentation.
Ethical & Licensing Information
Ethical Guidelines:
Mistral AI adheres to strict ethical guidelines, promoting responsible AI usage and development. The organization prioritizes transparency regarding the model's capabilities and limitations, actively encouraging developers to thoughtfully consider the ethical implications of deploying AI technologies in real-world applications.
Licensing:
Mistral Tiny is released under the permissive Apache 2.0 license. This open-source approach grants both commercial and non-commercial usage rights, significantly fostering community collaboration, innovation, and broad adoption across various industries.
Ready to harness the power of Mistral Tiny?
Access the Mistral Tiny API and start building your innovative, efficient applications today.
Get Mistral Tiny API Here!Frequently Asked Questions (FAQ)
Q: What is Mistral Tiny primarily designed for?
A: Mistral Tiny is a lightweight language model optimized for efficient text generation, summarization, and code completion tasks. It's particularly effective in resource-constrained environments that require rapid responses and low latency, such as chatbots and educational tools.
Q: What are the key technical specifications of Mistral Tiny?
A: It features 106.6 million parameters, requires only 0.4 GB of VRAM, supports an extensive context length of 131,072 tokens, and utilizes the LlamaTokenizer with a 32,000-token vocabulary for robust language processing.
Q: How does Mistral Tiny compare to Mistral Small or Mistral Large?
A: Mistral Tiny is built for speed and cost-effectiveness on simple tasks (over 85% accuracy). Mistral Small handles bulk tasks with moderate latency (72.2% accuracy), while Mistral Large excels in complex tasks, offering advanced reasoning and multilingual support (84.0% accuracy).
Q: What license is Mistral Tiny released under?
A: Mistral Tiny is released under the Apache 2.0 license, which grants broad permissions for both commercial and non-commercial usage, fostering open collaboration and innovation.
Q: What languages does Mistral Tiny support?
A: The model supports multiple languages, making it versatile for a global audience. These include English, French, German, Spanish, and Italian.
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