Mpt 30B Chat GGML
Meet MPT 30B Chat GGML, a powerful chatbot-like model for dialogue generation. Built by finetuning MPT-30B on various datasets, this model excels at multi-turn conversations and instruction following. With its 8K token context window, ALiBi support, and FlashAttention, MPT 30B Chat GGML is significantly more powerful than other LLMs. It's designed for efficient inference and training performance, making it a great choice for those who need fast and accurate results. While it's not perfect and may produce factually incorrect output, MPT 30B Chat GGML is a remarkable model that's poised to revolutionize the AI industry.
Table of Contents
Model Overview
Meet the MPT-30B-Chat model, a chatbot-like AI designed for dialogue generation. It’s built on top of the MPT-30B model, which has been fine-tuned for exceptional instruction following and multi-turn conversations.
Key Features
- 8k token context window: This model can handle longer conversations and more complex topics.
- ALiBi support: Attention with Linear Biases allows for more efficient and effective attention mechanisms.
- FlashAttention: A custom attention implementation that enables faster and more efficient training.
- High accuracy: MPT-30B-Chat has been trained to produce highly accurate and coherent responses.
Capabilities
The MPT-30B-Chat model is a chatbot-like model for dialogue generation. It’s designed to excel at multi-turn conversations, making it a great tool for tasks like customer support, language translation, and more.
Primary Tasks
This model is perfect for:
- Generating human-like responses in conversations
- Answering questions and providing helpful information
- Engaging in discussions and debates
- Creating content, such as articles, stories, and dialogues
Strengths
The MPT-30B-Chat model has several strengths that set it apart from other models:
- High accuracy: It’s trained on a large dataset and has a high accuracy rate, making it reliable for generating high-quality text.
- Long context window: It can handle long conversations and maintain context, making it perfect for tasks that require a deep understanding of the topic.
- Efficient inference: It’s optimized for fast inference, making it suitable for real-time applications.
Unique Features
This model has several unique features that make it stand out:
- 8k token context window: It can handle long conversations and maintain context, making it perfect for tasks that require a deep understanding of the topic.
- ALiBi support: It uses Attention with Linear Biases, which allows it to focus on specific parts of the input and generate more accurate responses.
- FlashAttention: It uses a custom attention mechanism that’s optimized for speed and accuracy.
Performance
MPT-30B-Chat shows remarkable performance in various tasks, especially in dialogue generation and multi-turn conversations. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast is MPT-30B-Chat? It can process inputs with a sequence length of up to 8192 tokens, and with the help of ALiBi, it can even handle longer sequences during finetuning and inference. This makes it suitable for tasks that require processing large amounts of text.
| Quant Method | Bits | Size | Max RAM Required | Use Case |
|---|---|---|---|---|
| q4_0 | 4 | 16.85 GB | 19.35 GB | 4-bit, lower accuracy, faster inference |
| q4_1 | 4 | 18.73 GB | 21.23 GB | 4-bit, higher accuracy than q4_0, quicker inference than q5 models |
| q5_0 | 5 | 20.60 GB | 23.10 GB | 5-bit, higher accuracy, higher resource usage, slower inference |
| q5_1 | 5 | 22.47 GB | 24.97 GB | 5-bit, even higher accuracy, resource usage, and slower inference |
| q8_0 | 8 | 31.83 GB | 34.33 GB | 8-bit, almost indistinguishable from float16, high resource use, slow |
Accuracy
MPT-30B-Chat achieves high accuracy in dialogue generation and multi-turn conversations. It has been fine-tuned on various datasets, including ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, Baize, and some generated datasets.
Efficiency
The model is designed to be efficient in terms of training and inference. It uses FlashAttention, ALiBi, and QK LayerNorm, which enable fast and efficient processing of inputs.
Limitations
While MPT-30B-Chat is a powerful tool, it’s not perfect. It can produce factually incorrect output and may generate lewd, biased, or otherwise offensive responses.
Lack of Factual Accuracy
MPT-30B-Chat can produce factually incorrect output. This means you shouldn’t rely solely on its responses for accurate information. It’s always a good idea to fact-check and verify the information it provides.
Biased or Offensive Outputs
MPT-30B-Chat was trained on various public datasets, which can sometimes contain biased or offensive content. While the team behind MPT-30B-Chat has made efforts to clean the data, it’s still possible for the model to generate outputs that are lewd, biased, or otherwise offensive.
Limited Context Length
MPT-30B-Chat has a maximum context length of 8K tokens. While this is a significant improvement over other models, it’s still limited. This means that MPT-30B-Chat might struggle with very long conversations or complex topics that require a lot of context.
Dependence on Data Quality
MPT-30B-Chat is only as good as the data it was trained on. If the data contains biases or inaccuracies, MPT-30B-Chat may learn and reproduce these flaws.
Not Suitable for All Use Cases
MPT-30B-Chat is designed for chatbot-like conversations, but it’s not suitable for all use cases. For example, it may not be the best choice for tasks that require a high degree of factual accuracy or for applications where biased or offensive outputs are unacceptable.
Format
MPT-30B-Chat uses a modified decoder-only transformer architecture. It was trained on a mix of datasets, including Airoboros/GPT4, Baize, Camel, GPTeacher, Guanaco, LongConversations, ShareGPT, and WizardLM.
Supported Data Formats
- Tokenized text sequences
- Maximum sequence length: 8192 tokens (input + output)
Special Requirements
- Requires
trust_remote_code=Truewhen loading the model - Supports FlashAttention and ALiBi (Attention with Linear Biases)
- Does not use positional embeddings or biases
Hyperparameters
| Hyperparameter | Value |
|---|---|
| n_parameters | 29.95B |
| n_layers | 48 |
| n_heads | 64 |
| d_model | 7168 |
| vocab size | 50432 |
| sequence length | 8192 |
Input/Output Handling
To use this model, you’ll need to preprocess your input text into tokenized sequences. You can use the AutoTokenizer from the transformers library to do this:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b')
Then, you can use the model.generate() method to generate text based on your input:
from transformers import pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(pipe('Here is a recipe for vegan banana bread:\\n', max_new_tokens=100, do_sample=True, use_cache=True))
Note that when running Torch modules in lower precision, it’s best practice to use the torch.autocast context manager.
Training Configuration
This model was trained on 64 H100s for about 7.6 hours using the MosaicML Platform. It was trained with sharded data parallelism using FSDP and used the AdamW optimizer.
