Unleash the Power of WizardLM-2: The Open-Source LLM Outperforming GPT-4

Wizard LM-2 is a significant milestone in the world of large language models (LLMs). This open-source model has demonstrated highly competitive performance compared to advanced proprietary works such as GPT-4 and Claude on the MT Benchmark, which measures the ability of LLMs to engage in coherent, informative, and engaging conversations.

The Wizard LM-2 model is a fine-tuned and preference-trained version of the Megatron-Turing NLG 22B model. It has been improved in terms of its performance on complex tasks, including chat, coding, multilingual reasoning, and agent-based applications.

The Wizard LM family now includes three new models: the Wizard LM-2 22B, the 70B parameter model, and the 7B parameter model. The Wizard LM-2 22B model has emerged as the most advanced, excelling in complex task performance, while the 70B model showcases top-tier reasoning capabilities, and the 7B model stands out for its speed and competitive performance.

The development of Wizard LM-2 involved several innovative techniques, including weighted sampling, progressive learning, the Evol-Instruct method for generating high-quality instructions, and the AI-Align-AI framework for collaborative model improvement. These methods have contributed to the model's impressive performance on the MT Benchmark and other evaluations.

To get started with Wizard LM-2, users can utilize the LM Studio platform. The model can be easily installed by copying the model card and pasting it into the search tab. This allows users to start chatting with the model and explore its capabilities.

Overall, the release of Wizard LM-2 represents a significant advancement in the field of open-source LLMs, challenging the dominance of proprietary models and showcasing the potential of collaborative research and development in the AI community.

The team behind Wizard LM has employed several advanced methods to develop the Wizard LM-2 model:

1. Data Pre-processing: They have analyzed and distributed different attributes in the new data sources to gain an initial understanding of the data. They have used weighted sampling to adjust the importance of various attributes in the training data based on experimental experience.

2. Progressive Learning: The training process has been broken into different stages, with more data slices being fed at each stage. This allows for the evolution of diverse instruction-response pairs.

3. AI-Aligned AI (AAA) Framework: Multiple LLMs are grouped together to teach and improve each other in an optimized manner through supervised learning and reinforcement learning.

4. Evol-Instruct: This component autonomously generates high-quality instructions and formats them through multiple iterations, enhancing the overall logic, correctness, and coherence of the model's responses.

5. Self-Teaching Methods: The AAA framework enables the Wizard LM-2 model to generate new training data, which is then used for reinforcement learning.

6. Supervised Learning: The model is trained using labeled data, stage-wise data processing, and split preference data to align with different slices for more efficient offline reinforcement learning.

These advanced methods, combined with the scaling up of the large language model, have resulted in the Wizard LM-2 model demonstrating highly competitive performance compared to leading proprietary works on the MT Benchmark.

The Wizard LM-2 model has demonstrated highly competitive performance compared to advanced proprietary language models like GPT-4 Turbo and CLA-3 on the MT Benchmark, which measures the ability to engage in coherent, informative, and engaging conversations.

The team behind Wizard LM has conducted both human and automatic evaluations to assess the model's capabilities. In the human preference evaluation, they collected a diverse set of real-world instructions covering writing, coding, mathematics, reasoning, agent tasks, and multilingual understanding. Annotators performed blind pairwise comparisons between Wizard LM-2 and baseline models, with the sources of the responses concealed.

The results indicate that Wizard LM-2 is consistently outperforming existing state-of-the-art models, including other open-source models. On the MT Benchmark, the model has demonstrated highly competitive performance, even compared to leading proprietary works.

Additionally, the team has provided detailed MT Benchmark performance data, showcasing Wizard LM-2's scores in comparison to various other models. This allows users to assess the model's strengths and weaknesses across different evaluation metrics.

To get started with Wizard LM-2, users can utilize the LM Studio platform. The model is available in three variants: the Wizard LM-2 AX 22B, the Wizard LM 70B, and the Wizard LM 7B. Users can simply copy the model card, search for the desired model in LM Studio, and proceed to download and use it within the platform's conversation interface.

To get started with the Wizard LM-2 model, you'll need to use LM Studio. First, go to the model card and copy the information for one of the three available models: Wizard LM-2 AX 22B, Wizard LM 70B, or Wizard LM 7B.

Next, open LM Studio and go to the search tab. Paste the model card information and click enter. You'll see the various versions of the Wizard LM-2 model available.

To install the model, simply download the version you want to use. Once downloaded, you can head over to the conversation tab in LM Studio and select the Wizard LM-2 model to start chatting with it.

The Wizard LM-2 model has demonstrated highly competitive performance on the MT benchmark, outperforming many leading proprietary models. It excels in complex task performance, top-tier reasoning capabilities, and speed, depending on the specific model size.

To learn more about the advancements and training methods used to develop the Wizard LM-2, be sure to check out the blog post and archived paper when they become available.

The introduction of Wizard LM2 represents a significant milestone in the development of large language models. This advanced model has demonstrated highly competitive performance compared to leading proprietary works, excelling in complex task performance, top-tier reasoning capabilities, and competitive speed.

The team behind Wizard LM has employed innovative techniques, such as weighted sampling, progressive learning, and AI-aligned AI frameworks, to enhance the model's capabilities. The incorporation of Evol-instruct, which autonomously generates and distributes high-quality instructions, has further improved the model's logic, correctness, and coherence.

Both human and automatic evaluations have shown that Wizard LM2 consistently outperforms existing state-of-the-art models, making it a promising contender in the field of large language models. With the availability of various Wizard LM models, users can choose the one that best suits their specific needs, whether it's the high-performance Wizard LM2 AX 22B, the powerful Wizard LM 70B, or the speed-focused Wizard LM 7B.

For those interested in exploring Wizard LM2, the process of installing and using the model locally is straightforward, thanks to the LM Studio platform. By following the provided instructions, users can easily access and interact with this cutting-edge language model.

As the AI landscape continues to evolve, the Wizard LM team's commitment to innovation and their iterative training efforts are likely to yield even more advancements in the near future. Researchers and developers should keep a close eye on the Wizard LM family as it continues to push the boundaries of large language model capabilities.