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README.md
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@@ -53,18 +53,16 @@ At the time of release, Zephyr 7B Gemma is the highest ranked 7B chat model on t
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In particular, on several categories of MT-Bench,
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However, on more complex tasks like coding and mathematics, Zephyr
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## Intended uses & limitations
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The model was initially fine-tuned on
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We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [
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You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66)
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Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
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@@ -76,25 +74,35 @@ Here's how you can run the model using the `pipeline()` function from 🤗 Trans
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import torch
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from transformers import pipeline
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pipe = pipeline(
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messages = [
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{
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"role": "system",
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"content": "
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},
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{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
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]
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```
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## Bias, Risks, and Limitations
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In particular, on several categories of MT-Bench, Zephyρ 7B Gemma has strong performance compared to larger open models like Llama2-Chat-70B:
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However, on more complex tasks like coding and mathematics, Zephyr 7B Gemma lags behind proprietary models and more research is needed to close the gap.
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## Intended uses & limitations
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The model was initially fine-tuned on the [DEITA 10K](https://huggingface.co/datasets/HuggingFaceH4/deita-10k-v0-sft) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
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We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) dataset, which contains 7k 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](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities.
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Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
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import torch
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="HuggingFaceH4/zephyr-7b-gemma",
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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messages = [
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{
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"role": "system",
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"content": "", # Model not yet trained for follow this
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},
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{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=128,
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do_sample=True,
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temperature=0.7,
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top_k=50,
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top_p=0.95,
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stop_sequence="<|im_end|>",
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)
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print(outputs[0]["generated_text"][-1]["content"])
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# It is not possible for a human to eat a helicopter in one sitting, as a
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# helicopter is a large and inedible machine. Helicopters are made of metal,
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# plastic, and other materials that are not meant to be consumed by humans.
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# Eating a helicopter would be extremely dangerous and would likely cause
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# serious health problems, including choking, suffocation, and poisoning. It is
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# important to only eat food that is safe and intended for human consumption.
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```
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## Bias, Risks, and Limitations
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