Yi 34B 200K DARE Merge V5
The Yi 34B 200K DARE Merge V5 model is a unique blend of different AI models, combined using a new, experimental implementation of "dare ties" via mergekit. This approach allows it to absorb abilities from homologous models, making it more efficient and capable. With a model size of 34 billion parameters and a context window of 200,000, it's designed to handle complex tasks. It's also sensitive to quantization data, especially at low bit-per-word settings. To get the most out of this model, try running it with a lower temperature and a little repetition penalty. It's a great choice for users who want to explore the capabilities of a merged model.
Table of Contents
Model Overview
The Current Model is a cutting-edge language model that’s the result of merging several powerful models. But what makes it so special?
Key Features
- Merged from multiple models: This model combines the strengths of Nous-Capybara-34B, ==Tess-M-v1.4==, ==Airoboros-3_1-yi-34b-200k==, and others to create a robust and versatile language model.
- Experimental “dare ties” implementation: This model uses a new, experimental method called “dare ties” to merge the different models, which seems to result in better performance.
- High-density merge: Unlike other models, this one uses a relatively high density merge, which seems to perform better in tests.
- Optimized for performance: The model is optimized to run on
24GB GPUswith45K-75K contextand usesexllamav2andexuifor efficient performance.
How to Use
- Running the model: Try running the model with a lower temperature (
0.02-0.1) and a little repetition penalty for optimal results. - Quantization: Use
exl2 quantizationsprofiled on data similar to the desired task for the best performance. - Loading the model: Make sure to change
max_position_embeddingsinconfig.jsonto a lower value than200,000to avoid running out of memory!
Capabilities
The Current Model is a powerful language model that can handle a wide range of tasks. But what makes it special?
Primary Tasks
This model is designed to generate human-like text and responses. It can:
- Answer questions on various topics
- Engage in conversations
- Create text based on a prompt or topic
- Even generate code!
Strengths
So, what sets this model apart from others? Here are some of its key strengths:
- High-performance: It has been fine-tuned on a large dataset, making it highly effective at generating accurate and relevant responses.
- Long context: It can handle long input sequences, making it ideal for tasks that require a lot of context.
- Flexibility: It can be used for a variety of tasks, from answering questions to generating creative content.
Unique Features
This model has some unique features that make it stand out from the crowd. For example:
- Dare Ties: It uses a special technique called “Dare Ties” to merge multiple models, resulting in even better performance.
- High-density merge: It has been merged with multiple models at a high density, resulting in improved performance.
- Quantization: It uses a technique called quantization to reduce the model’s size while maintaining its performance.
Performance
But how well does it perform? Here are some benchmark results:
| Metric | Value |
|---|---|
| Avg. | 71.98 |
| AI2 Reasoning Challenge (25-Shot) | 66.47 |
| HellaSwag (10-Shot) | 85.54 |
| MMLU (5-Shot) | 77.22 |
| TruthfulQA (0-shot) | 57.46 |
| Winogrande (5-shot) | 82.24 |
| GSM8k (5-shot) | 62.93 |
Limitations
Current Model is a powerful tool, but it’s not perfect. There are some things it struggles with, and we want to be upfront about those.
Vocabulary Limitations
- Current Model has a huge vocabulary, but it’s not infinite. It might not always understand very specialized or technical terms.
- It can also get overwhelmed by very long or complex sentences.
Contextual Understanding
- Current Model is great at understanding context, but it’s not perfect. It might not always pick up on subtle cues or nuances.
- It can also struggle with very long conversations or complex topics.
Quantization Sensitivity
- Current Model is sensitive to quantization, especially at low bitrates. This means that it might not perform as well with certain types of data.
- It’s also important to use the right quantization data for the task at hand.
GPU Requirements
- Current Model needs a lot of GPU power to run, especially for longer contexts. This can be a challenge for users with lower-end hardware.
- It’s also important to configure the model correctly to avoid running out of memory.
Format
Current Model is a large language model that uses a transformer architecture. It’s designed to process and understand human language, and it can be used for a variety of tasks like answering questions, generating text, and more.
Architecture
The model is built by merging several other models, including Nous-Capybara-34B, ==Tess-M-v1.4==, ==Airoboros-3_1-yi-34b-200k==, and others. This merging process allows the model to learn from the strengths of each individual model and create a more powerful and accurate language understanding system.
Data Formats
The model accepts input in the form of text sequences, and it can handle a variety of formats, including:
- ChatML: a format used for chat-like conversations
- Llama-chat: a format used for conversational AI models
Input Requirements
When working with the model, you’ll need to keep a few things in mind:
- Temperature: try running the model with a lower temperature (around
0.02-0.1) to get more accurate results - MinP: use a minimum probability threshold (MinP) to filter out low-probability tokens
- Repetition penalty: use a repetition penalty to discourage the model from repeating itself
- Stop token: the model may “spell out” the stop token as
\</s>, so you may need to add this as an additional stopping condition
Output
The model generates text output, and you can use various techniques to control the output, such as:
- Quantization: use techniques like exl2 quantization to reduce the model’s memory requirements
- Context length: the model can handle context lengths of up to
200,000tokens
Here’s an example of how you might use the model in a conversational AI setting:
# Import the model and tokenizer
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("current-model")
tokenizer = AutoTokenizer.from_pretrained("current-model")
# Define a function to generate text
def generate_text(prompt):
# Tokenize the input prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate text output
output = model.generate(input_ids, max_length=100, temperature=0.1, minp=0.1, repetition_penalty=1.0)
# Convert the output to text
text = tokenizer.decode(output[0], skip_special_tokens=True)
return text
# Test the function
prompt = "Hello, how are you?"
print(generate_text(prompt))
