--- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - text: "<|system|>\nYou are a pirate chatbot who always responds with Arr!\n<|user|>\nThere's a llama on my lawn, how can I get rid of him?\n<|assistant|>\n" output: text: "Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr!" pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench --- Zephyr Logo # Neuronx model for [Zephyr 7B β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) This repository contains [**AWS Inferentia2**](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoints for [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). You can find detailed information about the base model on its [Model Card](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). This model has been exported to the `neuron` format using specific `input_shapes` and `compiler` parameters detailed in the paragraphs below. Please refer to the 🤗 `optimum-neuron` [documentation](https://huggingface.co/docs/optimum-neuron/main/en/guides/models#configuring-the-export-of-a-generative-model) for an explanation of these parameters. ## Usage on Amazon SageMaker _coming soon_ ## Usage with 🤗 `optimum-neuron` ```python from optimum.neuron import pipeline p = pipeline('text-generation', 'aws-neuron/zephyr-7b-seqlen-2048-bs-4-cores-2') # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` This repository contains tags specific to versions of `neuronx`. When using with 🤗 `optimum-neuron`, use the repo revision specific to the version of `neuronx` you are using, to load the right serialized checkpoints. ## Arguments passed during export **input_shapes** ```json { "batch_size": 4, "sequence_length": 2048, } ``` **compiler_args** ```json { "auto_cast_type": "fp16", "num_cores": 2, } ```