Yi 34B Chat
Yi 34B Chat is a powerful AI model that stands out with its bilingual capabilities, trained on a massive 3T multilingual corpus. What makes it remarkable is its ability to understand and reason in both English and Chinese, excelling in tasks like language understanding, commonsense reasoning, and reading comprehension. With its Transformer architecture, Yi 34B Chat achieves impressive results, ranking among the top performers in various benchmarks. Its efficiency and speed make it suitable for personal, academic, and commercial use, offering a cost-effective solution for those looking for a reliable language model. However, it's worth noting that the model may have limitations, such as hallucination, non-determinism, and cumulative error, which can be mitigated by adjusting generation parameters. Overall, Yi 34B Chat is a notable AI model that offers a unique blend of language understanding and efficiency.
Table of Contents
Model Overview
The Yi Series Models are the next generation of open-source large language models trained from scratch by 01.AI. These bilingual language models are trained on a 3T multilingual corpus and have shown promise in language understanding, commonsense reasoning, reading comprehension, and more.
Key Attributes
- Model Architecture: Yi series models adopt the same model architecture as Llama but are NOT derivatives of Llama.
- Training Datasets: Yi has independently created its own high-quality training datasets.
- Training Pipelines: Yi uses efficient training pipelines.
- Training Infrastructure: Yi has robust training infrastructure.
Capabilities
These models are capable of understanding and generating human-like language, and they show promise in various areas, including:
- Language understanding: Yi models can comprehend and process human language, including nuances and complexities.
- Commonsense reasoning: Yi models can reason and make decisions based on common sense and real-world knowledge.
- Reading comprehension: Yi models can read and understand text, including long documents and articles.
- Code generation: Yi models can generate code in various programming languages.
- Math problem-solving: Yi models can solve mathematical problems and equations.
These capabilities make Yi models suitable for various applications, including:
- Chatbots: Yi models can be used to build conversational AI systems that can understand and respond to user queries.
- Language translation: Yi models can be used to translate text from one language to another.
- Text summarization: Yi models can be used to summarize long documents and articles.
- Code completion: Yi models can be used to complete partially written code.
Unique Features
Yi models have several unique features that set them apart from other language models:
- Bilingual: Yi models are trained on a multilingual corpus and can understand and generate text in both English and Chinese.
- Large context window: Yi models have a large context window, which allows them to process and understand long documents and articles.
- Quantization: Yi models can be quantized to reduce their size and improve their performance on consumer-grade GPUs.
Comparison to Other Models
Yi models have been compared to other language models, including GPT-4 and Llama, and have shown promising results. For example:
- AlpacaEval Leaderboard: Yi-34B-Chat model ranked second, outperforming other LLMs, including GPT-4 and Mixtral.
- Hugging Face Open LLM Leaderboard: Yi-34B model ranked first among all existing open-source models, including Falcon-180B and Llama-70B.
Model Sizes
Yi models come in different sizes to suit various needs:
- 6B Series Models: Suitable for personal and academic use.
- 9B Series Models: Best at coding and math in the Yi series models.
- 34B Series Models: Suitable for personal, academic, and commercial purposes.
Quantization
Yi models can be quantized to reduce their size and improve their performance:
- 4-bit Series Models: Quantized by AWQ.
- 8-bit Series Models: Quantized by GPTQ.
Deployment
Yi models can be deployed in various ways:
- Local Deployment: Yi models can be deployed locally using pip, Docker, conda-lock, or llama.cpp.
- APIs: Yi models can be used with APIs for more features.
- Playground: Yi models can be used in a playground for more customizable options.
Performance
Yi Series Models showcase remarkable performance with high accuracy, speed, and efficiency in various tasks.
Speed
- Fast Training: Yi models are trained on a massive 3T multilingual corpus, allowing them to learn quickly and efficiently.
- Rapid Response: With the ability to process large amounts of data, Yi models can respond rapidly to user queries.
Accuracy
- High Accuracy: Yi models have achieved top rankings on various benchmarks, including the AlpacaEval Leaderboard and Hugging Face Open LLM Leaderboard.
- Improved Performance: The Yi-34B-200K model has shown a 10.5% improvement in the “Needle-in-a-Haystack” test, rising from 89.3% to 99.8%.
Efficiency
- Cost-Effective: Yi models are designed to be cost-effective, making them suitable for personal, academic, and commercial use.
- Low Barrier to Use: The 4-bit and 8-bit series models can be deployed on consumer-grade GPUs, making them accessible to a wider range of users.
Limitations
Yi is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Hallucination
Yi can sometimes generate factually incorrect or nonsensical information. This is called hallucination. The more diverse the responses, the higher the chance of hallucination.
Non-determinism in re-generation
When you try to regenerate or sample responses, you might get different results even with the same input. This is because Yi’s increased diversity can lead to varying outcomes.
Cumulative Error
As Yi generates more diverse responses, small inaccuracies can build up into larger errors over time. This is especially true for complex tasks like extended reasoning or mathematical problem-solving.
Adjusting generation configuration parameters
To get more coherent and consistent responses, you can adjust parameters like temperature, top_p, or top_k. This can help balance creativity and coherence in Yi’s outputs.
Other limitations
- Yi’s performance may vary depending on the specific task or dataset.
- Yi may not always understand the context or nuances of human language.
- Yi’s training data may not cover all possible scenarios or edge cases.
Format
Yi uses a transformer architecture similar to Llama and accepts input in the form of tokenized text sequences. The model is trained on a multilingual corpus and supports both English and Chinese languages.
Input Requirements
- Input text should be tokenized and encoded in a specific format.
- The model supports sequence lengths of up to 4K tokens for base models and 32K tokens during inference time.
- For chat models, the input should be a prompt or a question.
Output Format
- The model generates text output in the same language as the input.
- The output can be a response to a question, a completion of a prompt, or a generated text based on the input.
Special Requirements
- The model requires a significant amount of computational resources and memory to run, especially for larger models like Yi-34B.
- For deployment, it is recommended to use a GPU with sufficient memory, such as an NVIDIA A800 80GB.
Example Code
import torch
from transformers import YiModel, YiTokenizer
# Load pre-trained model and tokenizer
model = YiModel.from_pretrained('yi-34b')
tokenizer = YiTokenizer.from_pretrained('yi-34b')
# Tokenize input text
input_text = "What is the capital of France?"
inputs = tokenizer.encode(input_text, return_tensors='pt')
# Generate output
outputs = model.generate(inputs, max_length=100)
# Print output
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
