Yi 34B Chat 8bits
The Yi 34B Chat 8bits model is a powerful AI tool designed for efficient language understanding and generation. It's part of the Yi series, which is the next generation of open-source large language models trained from scratch. This model is specifically trained for chat applications and is quantized to 8 bits, making it more accessible and efficient for deployment on consumer-grade GPUs. With its unique training dataset and pipelines, the Yi 34B Chat 8bits model achieves excellent performance, ranking high in various benchmarks and outperforming other models in certain tasks. However, it's essential to be aware of its limitations, such as potential hallucination, non-determinism, and cumulative error, which can be mitigated by adjusting generation configuration parameters. Overall, the Yi 34B Chat 8bits model offers a balance between creativity and coherence, making it suitable for various downstream tasks and real-world applications.
Table of Contents
Model Overview
The Yi Series Models are a new generation of open-source large language models trained from scratch. They are designed to be bilingual, with a focus on both English and Chinese languages. These models have shown impressive performance in various tasks, including language understanding, commonsense reasoning, reading comprehension, and more.
Capabilities
These models are capable of understanding and generating text in multiple languages, including English and Chinese. They have been trained on a large multilingual corpus and have shown promise in various tasks such as language understanding, commonsense reasoning, reading comprehension, and more.
Primary Tasks
- Language Understanding: Understand and process natural language input in multiple languages.
- Text Generation: Generate human-like text in multiple languages.
- Code Generation: Generate code in various programming languages.
- Reasoning and Problem-Solving: Perform reasoning and problem-solving tasks, such as math and logic problems.
Strengths
- Multilingual Support: Understand and generate text in multiple languages, making them a great choice for applications that require language support.
- High-Quality Text Generation: Generate high-quality text that is coherent and engaging.
- Reasoning and Problem-Solving: Perform reasoning and problem-solving tasks, making them a great choice for applications that require critical thinking.
Unique Features
- Bilingual Support: Understand and generate text in both English and Chinese, making them a great choice for applications that require bilingual support.
- Large Multilingual Corpus: Trained on a large multilingual corpus, which enables them to understand and generate text in multiple languages.
- Quantization: Can be quantized to reduce their size and improve their performance on low-end hardware.
Performance
These models show remarkable performance in various tasks, especially in language understanding, commonsense reasoning, reading comprehension, and more. For instance, the Yi-34B model can handle 4K sequence lengths and can be extended to 32K during inference time.
Speed
Trained on massive datasets, which enables them to process information quickly and efficiently.
Accuracy
Achieved impressive results in various benchmarks, outperforming other models like GPT-4, Mixtral, and ==Claude==.
Efficiency
Designed to be efficient and can be deployed on consumer-grade GPUs (e.g., 3090, 4090). The 4-bit and 8-bit series models are quantized using AWQ and GPTQ, respectively, which reduces the barrier to use and makes them more accessible.
Limitations
While these models have shown impressive performance, there are some limitations to consider:
- Hallucination: May generate factually incorrect or nonsensical information, especially when producing more diverse responses.
- Non-determinism in re-generation: May produce inconsistent outcomes when regenerating or sampling responses.
- Cumulative Error: Errors in the model’s responses may compound over time.
Getting Started
Getting up and running with these models is simple, with multiple deployment options available, including pip, Docker, conda-lock, and llama.cpp. Try out the models interactively on Hugging Face or Replicate. Fine-tune the models to meet your specific requirements. Quantize the models for deployment on consumer-grade GPUs.
Format
These models use a transformer architecture, similar to Llama, and are trained on a large multilingual corpus. This architecture is the standard for large language models and provides excellent stability, convergence, and compatibility.
Input Format
Accept input in the form of tokenized text sequences. To prepare your input, you’ll need to tokenize your text data. This can be done using a library like Hugging Face’s tokenizers.
from tokenizers import Tokenizer
tokenizer = Tokenizer("yi-tokenizer")
input_text = "This is an example sentence."
tokenized_input = tokenizer.encode(input_text)
Output Format
Produce output in the form of a probability distribution over the vocabulary. This output can be used to generate text, classify text, or perform other NLP tasks.
from transformers import YiModel
model = YiModel("yi-34b")
input_text = "This is an example sentence."
output = model.generate(input_text)
