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New Zephyr-7B LLM fine-tuned, beats Llama-2 70B

New Zephyr-7B LLM fine-tuned Mistral-7B AI model

The world of artificial intelligence has witnessed another remarkable milestone with the release of the new Zephyr-7B AI model on Hugging Face. This innovative model is a fine-tuned successor to the original Mistral 7B, and it has managed to outperform larger 70 billion parameter models, even while being uncensored. The company has also unveiled a comprehensive technical report, offering a detailed overview of the training process of the model. Try out the Zephyr 7B Beta new here.

Direct preference optimization (DPO)

The Zephyr-7B model has been trained using a three-step strategy. The first step involves distilled supervised fine-tuning using the Ultra Chat dataset. This dataset, comprising 1.47 million multi-dialogues generated by GPT 3.5 Turbo, underwent a rigorous cleaning and filtering process, leaving only 200,000 examples. The distilled supervised fine-tuning process involves a teacher-student model dynamic, with a larger model like GPT 3.5 playing the role of the teacher and Zephyr-7B as the student. The teacher model generates a conversation based on a prompt, which is then used to fine-tune the student model, Zephyr-7B.

Zephyr-7B beats Llama-2 70B

The second step of the training strategy is AI feedback. This step utilizes the Ultra Feedback dataset, consisting of 64,000 different prompts. Four different models generate responses to each prompt, which are then rated by GP4 based on honesty and helpfulness. This process aids in refining the model’s responses, contributing to its overall performance.

Other articles we have written that you may find of interest on the subject of Zephyr and Mistral large language models:

The final step of the training strategy involves training another model using the dataset created with a winner and loser. This step further solidifies the learning of the Zephyr-7B model, ensuring that it can generate high-quality, reliable responses.

The performance of the Zephyr-7B model has been impressive, outperforming all other 7 billion models and even larger models like the Falcon 40 billion and Llama 2 70 billion models. However, it’s important to note that the model’s performance varies depending on the specific task. For instance, it lags behind in tasks like coding and mathematics. Thus, users should choose a model based on their specific needs, as the Zephyr-7B model may not be the best fit for all tasks.

Zephyr-7B LLM

One unique aspect of the Zephyr-7B model is its uncensored nature. While it is uncensored to a certain extent, it has been designed to advise against illegal activities when prompted, ensuring that it maintains ethical guidelines in its responses. This aspect is crucial in maintaining the integrity and responsible use of the model.

Running the Zephyr-7B model can be done locally using LMStudio or UABA Text Generation WebUI. This provides users with the flexibility to use the model in their preferred environment, enhancing its accessibility and usability.

The Zephyr-7B model is a significant addition to the AI landscape. Its unique training strategy, impressive performance, and uncensored nature set it apart from other models. However, its performance varies depending on the task at hand, so users should choose a model that best suits their specific needs. The company’s active Discord server provides a platform for discussions related to generative AI, fostering a community of learning and growth. As the field of AI continues to evolve, it will be exciting to see what future iterations of models like Zephyr-7B will bring.

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Llama 2 70B vs Zephyr-7B LLM models compared

Llama 2 70B vs Zephyr-7B LLM models compared

A new language model known as Zephyr has been created. The Zephyr-7B-α large language model, has been designed to function as helpful assistants, providing a new level of interaction and utility in the realm of AI. This Llama 2 70B vs Zephyr-7B overview guide and comparison video will provide more information on the development and performance of Zephyr-7B. Exploring its training process, the use of Direct Preference Optimization (DPO) for alignment, and its performance in comparison to other models.  In Greek mythology, Zephyr or Zephyrus is the god of the west wind, often depicted as a gentle breeze bringing in the spring season.

Zephyr-7B-α, the first model in the Zephyr series, is a fine-tuned version of Mistral-7B-v0.1. The model was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO), a technique that has proven to be effective in enhancing the performance of language models. Interestingly, the developers found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this also means that the model is likely to generate problematic text when prompted to do so, and thus, it is recommended for use only for educational and research purposes.

Llama 2 70B vs Zephyr-7B

If you are interested in learning more the Prompt Engineering YouTube channel has created a new video comparing it with  the massive Llama 2 70B AI model.

 Previous articles we have written that you might be interested in on the subject of the Mistral and Llama 2 AI models :

The initial fine-tuning of Zephyr-7B-α was carried out on a variant of the UltraChat dataset. This dataset contains a diverse range of synthetic dialogues generated by ChatGPT, providing a rich and varied source of data for training. The model was then further aligned with TRL’s DPOTrainer on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4.

It’s important to note that Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT. This means that the model can produce problematic outputs, especially when prompted to do so. The size and composition of the corpus used to train the base model (mistralai/Mistral-7B-v0.1) are unknown, but it is likely to have included a mix of Web data and technical sources like books and code.

When it comes to performance, Zephyr-7B-α holds its own against other models. A comparison with the Lama 270 billion model, for instance, shows that Zephyr’s development and training process has resulted in a model that is capable of producing high-quality outputs. However, as with any AI model, the quality of the output is largely dependent on the quality and diversity of the input data.

Testing of Zephyr’s writing, reasoning, and coding abilities has shown promising results. The model is capable of generating coherent and contextually relevant text, demonstrating a level of understanding and reasoning that is impressive for a language model. Its coding abilities, while not on par with a human coder, are sufficient for basic tasks and provide a glimpse into the potential of AI in the field of programming.

The development and performance of the Zephyr-7B-α AI model represent a significant step forward in the field of AI language models. Its training process, use of DPO for alignment, and performance in comparison to other models all point to a future where AI models like Zephyr could play a crucial role in various fields, from education and research to programming and beyond. However, it’s important to remember that Zephyr, like all AI models, is a tool and its effectiveness and safety depend on how it is used and managed.

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