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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/fastchat-t5-3b-v1.0',
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/fastchat-t5-3b-v1.0",
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
💬 Vicuna FastChat-T5: An Advanced Open-Source Chatbot Model
Developed by LM-SYS (primarily Dacheng Li, Lianmin Zheng, and Hao Zhang) and released in April 2023, FastChat-T5 (3B) is an innovative open-source chatbot designed for conversational AI. This text-based model significantly enhances the Flan-t5-xl model (3 billion parameters) through meticulous fine-tuning on a vast collection of real-world conversations.
It employs an encoder-decoder transformer architecture to generate highly relevant and natural responses to user inputs, making it a powerful tool for various natural language processing (NLP) tasks.
✅ Key Features at a Glance
- Architecture: Encoder-decoder transformer for robust understanding and generation.
- Training Data: Fine-tuned on an extensive dataset of 70,000 conversations from ShareGPT.
- Response Generation: Utilizes autoregressive generation for coherent and context-aware outputs.
- Optimization: Features an optimized learning rate and warmup ratio during fine-tuning for superior performance.
- License: Available under the permissive Apache License 2.0, supporting broad usage.
💻 Intended Applications & Language Support
Designed for Versatility: FastChat-T5 is purpose-built for commercial chatbot applications and cutting-edge research in natural language processing. Its capabilities extend to generating dynamic responses in conversational agents, enhancing customer service, and supporting various other NLP tasks.
Primary Language: The model primarily supports English. While it may process other languages, accuracy could be diminished due to the predominant English nature of its training data.
⚒ Technical Architecture & Training Insights
Core Architecture: FastChat-T5 leverages an advanced encoder-decoder transformer architecture.
- The encoder processes input text bidirectionally, creating rich hidden representations.
- The decoder then strategically uses cross-attention on these representations to generate responses autoregressively, starting from an initial token.
Training Data Breakdown:
- Source: An extensive collection of 70,000 conversations meticulously gathered from ShareGPT.com.
- Nature: User-shared conversational data, pre-processed into effective question-answer pairs.
- Knowledge Cutoff: The model's knowledge base is current up to April 2023.
Diversity & Potential Biases: The training data reflects a wide array of conversational scenarios and user interactions. However, it's crucial to acknowledge that the dataset may inherit certain biases present in user-shared content on ShareGPT, which could influence model outputs.
📊 Performance Benchmarks
Accuracy Insights: Despite having fewer parameters, FastChat-T5 consistently demonstrates superior performance compared to models like Dolly-V2-12B in several key areas. It excels in:
- Generic Task Types
- Role-Playing Scenarios
- Common Sense Reasoning
- Counterfactual Tasks
Speed & Efficiency: The model is finely optimized for efficient inference, particularly on GPU-enabled systems. Its fine-tuning process employed a sophisticated cosine learning rate schedule combined with a warmup ratio of 0.03, contributing to its speed and stability.
Robustness & Limitations: FastChat-T5 exhibits strong robustness across diverse input types. However, its current capabilities show limitations in specialized domains such as programming and advanced mathematical tasks, where it tends to score lower compared to models tailored for these areas.
💼 Usage Guidelines & Ethical Considerations
Code Samples: Developers can integrate FastChat-T5 into their applications. An example snippet, similar to OpenAI's chat completion API, might look like this:
import openai
openai.api_base = "YOUR_FASTCHAT_API_BASE_URL" # e.g., "http://localhost:8000/v1"
openai.api_key = "EMPTY" # Not strictly required for local FastChat deployments
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Tell me a joke."},
]
completion = openai.ChatCompletion.create(
model="lmsys/fastchat-t5-3b-v1.0", # Specify the model identifier
messages=messages,
temperature=0.7,
)
print(completion.choices[0].message.content)
Note: The `snippet` tag from the original content has been interpreted as a placeholder for a typical API usage example. This code is illustrative and may require specific FastChat-T5 server setup.
Ethical Use & Bias Awareness: As with any AI model trained on large datasets, FastChat-T5 may inadvertently inherit biases present in its ShareGPT training data. Users are strongly advised to exercise caution and responsibility, being mindful of potential ethical issues, including the generation of biased or harmful outputs. Regular monitoring and human oversight are recommended.
License Information: FastChat-T5 operates under the Apache License 2.0, a highly flexible open-source license that permits both commercial and non-commercial deployment and modification.
❓ Frequently Asked Questions (FAQs) about FastChat-T5
Q1: What is FastChat-T5 and who developed it?
A1: FastChat-T5 is an open-source chatbot model based on the Flan-t5-xl, fine-tuned on ShareGPT conversations. It was developed by LM-SYS, notably Dacheng Li, Lianmin Zheng, and Hao Zhang, and released in April 2023.
Q2: What kind of architecture does FastChat-T5 use?
A2: It utilizes an encoder-decoder transformer architecture, allowing it to process input text bidirectionally and generate responses autoregressively.
Q3: Can FastChat-T5 be used for commercial projects?
A3: Yes, FastChat-T5 is licensed under the Apache License 2.0, which explicitly permits both commercial and non-commercial use, making it suitable for a wide range of applications.
Q4: What are the main strengths and weaknesses of FastChat-T5's performance?
A4: It excels in generic tasks, role-playing, common sense, and counterfactual tasks, often outperforming larger models. However, it shows limitations in specialized areas like programming and advanced mathematics.
Q5: What ethical considerations should users be aware of when using FastChat-T5?
A5: Users should be aware that the model may inherit biases from its ShareGPT training data. It's crucial to use the model responsibly, monitor outputs for potential biases or harmful content, and implement human oversight.
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