zephyr-beta

About

What zephyr-beta is

should probably proofread and complete it, then remove this comment. -- Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the technical report. Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: MIT - Finetuned from model: mistralai/Mistral-7B-v0.1 Model Sources - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks: In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with 🤗 TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities. You can find the datasets used for training Zephyr-7B-β here Here's how you can run the model using the pipeline() function from 🤗 Transformers: Bias, Risks, and Limitations

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys zephyr-beta for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (zephyr-beta below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"zephyr-beta","messages":[{"role":"user","content":"Hello"}]}'

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