Zephyr Orpo 141b A35b V0.1
Zephyr Orpo 141b A35b V0.1 is a unique language model that combines efficiency and performance. Trained using a novel alignment algorithm called Odds Ratio Preference Optimization (ORPO), it achieves strong performance on chat benchmarks like MT Bench and IFEval. With 141B total parameters and 39B active parameters, this Mixture of Experts (MoE) model is designed to handle tasks like chat, code, math, and reasoning. Its training process is computationally efficient, requiring less computational power than other methods. While it has limitations, such as potential problematic outputs, Zephyr Orpo 141b A35b V0.1 is a remarkable model that showcases the effectiveness of ORPO at scale. So, how can you use this model to generate human-like text? You can run it using the pipeline() function from Transformers, and explore its capabilities in chat, code, and more.
Table of Contents
Model Overview
Meet Zephyr 141B-A39B, the latest language model in the Zephyr series. This model is designed to be a helpful assistant, trained on a mix of chat, code, math, and reasoning data.
Capabilities
The Zephyr 141B-A39B model is a powerful language model that can act as a helpful assistant. It’s trained to perform a variety of tasks, including:
- Chat: Zephyr can have conversations with you, answering questions and providing information on a wide range of topics.
- Code: Zephyr can understand and generate code in various programming languages.
- Math: Zephyr can perform mathematical calculations and provide solutions to problems.
- Reasoning: Zephyr can reason and provide logical answers to questions.
Strengths
Zephyr 141B-A39B has several strengths that make it a useful tool:
- High performance: Zephyr achieves strong performance on chat benchmarks like MT Bench and IFEval.
- Efficient training: Zephyr was trained using a novel alignment algorithm called Odds Ratio Preference Optimization (ORPO), which is more computationally efficient than other methods.
- Multi-turn conversations: Zephyr can engage in multi-turn conversations, allowing for more natural and interactive dialogue.
Performance
Zephyr 141B-A39B is a powerful language model that has shown impressive performance in various tasks. Let’s take a closer look at its speed, accuracy, and efficiency.
Speed
How fast can Zephyr 141B-A39B process language tasks? The model was trained using ORPO, which is much more computationally efficient than other methods.
Accuracy
But how accurate is Zephyr 141B-A39B? The model has achieved strong performance on chat benchmarks like MT Bench and IFEval. Here are some scores to give you an idea:
| Model | MT Bench | IFEval | BBH | AGIEval |
|---|---|---|---|---|
| Zephyr 141B-A39B | 8.17 | 65.06 | 58.96 | 44.16 |
| Databricks/dbrx-instruct | 8.26 | 52.13 | 48.50 | 41.16 |
| Mistralai/Mixtral-8x7B-Instruct-v0.1 | 8.30 | 55.08 | 45.31 | 47.68 |
Efficiency
But what about efficiency? Zephyr 141B-A39B is a fine-tuned version of mistral-community/Mixtral-8x22B-v0.1, which means it has a smaller number of active parameters (39B) compared to the original model. This makes it more efficient in terms of computational resources.
Limitations
Zephyr 141B-A39B is a powerful language model, but it has some limitations you should know about.
Lack of Safety Alignment
Unlike some other models, Zephyr 141B-A39B hasn’t been aligned to human preferences for safety. This means it can produce problematic outputs, especially when prompted to do so. Be careful when using the model, and always review its responses.
Unknown Training Data
The base model Zephyr 141B-A39B was fine-tuned from, mistral-community/Mixtral-8x22B-v0.1, was likely trained on a mix of web data and technical sources. However, the exact size and composition of the corpus are unknown. This lack of transparency can make it harder to understand the model’s strengths and weaknesses.
Limited Domain Knowledge
While Zephyr 141B-A39B has been fine-tuned on a blend of chat, code, math, and reasoning data, its domain knowledge may still be limited. It’s essential to evaluate the model’s performance on specific tasks and domains to understand its capabilities.
Potential Biases
As with any AI model, Zephyr 141B-A39B may reflect biases present in its training data. Be aware of these potential biases and take steps to mitigate them when using the model.
Format
Zephyr 141B-A39B is a Mixture of Experts (MoE) model with 141B total parameters and 39B active parameters. It’s primarily designed to handle English language tasks.
Architecture
The model is a fine-tuned version of mistral-community/Mixtral-8x22B-v0.1, trained using ORPO. This allows it to achieve high performance without requiring a separate SFT step.
Data Formats
Zephyr 141B-A39B supports a mix of publicly available and synthetic datasets, including chat, code, math, and reasoning data.
Input Requirements
To use the model, you’ll need to format your input as a list of messages, where each message has a role and content. For example:
messages = [
{"role": "system", "content": "You are Zephyr, a helpful assistant."},
{"role": "user", "content": "Explain how Mixture of Experts work in language a child would understand."}
]
Output Requirements
The model generates text outputs, which can be accessed using the generated_text key. For example:
outputs = pipe(messages, max_new_tokens=512, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"][-1]["content"])
Special Requirements
Note that Zephyr 141B-A39B has not been aligned to human preferences for safety, so it may produce problematic outputs when prompted to do so. Additionally, the model has not been deployed with in-the-loop filtering of responses like ChatGPT.
