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--- |
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tags: |
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- generated_from_trainer |
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license: mit |
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datasets: |
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- HuggingFaceH4/ultrachat_200k |
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- HuggingFaceH4/ultrafeedback_binarized |
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language: |
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- en |
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base_model: mistralai/Mistral-7B-v0.1 |
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widget: |
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- text: "<|system|>\nYou are a pirate chatbot who always responds with Arr!</s>\n<|user|>\nThere's a llama on my lawn, how can I get rid of him?</s>\n<|assistant|>\n" |
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output: |
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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!" |
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pipeline_tag: text-generation |
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model-index: |
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- name: zephyr-7b-beta |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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name: normalized accuracy |
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value: 62.03071672354948 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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name: normalized accuracy |
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value: 84.35570603465445 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Drop (3-Shot) |
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type: drop |
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split: validation |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: f1 |
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name: f1 score |
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value: 9.662437080536909 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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|
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 57.44916942762855 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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|
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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name: accuracy |
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value: 12.736921910538287 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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|
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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name: accuracy |
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value: 61.07 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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|
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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name: accuracy |
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value: 77.74269928966061 |
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source: |
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name: Open LLM Leaderboard |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta |
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|
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AlpacaEval |
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type: tatsu-lab/alpaca_eval |
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metrics: |
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- type: unknown |
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name: win rate |
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value: 0.9060 |
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source: |
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url: https://tatsu-lab.github.io/alpaca_eval/ |
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|
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MT-Bench |
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type: unknown |
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metrics: |
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- type: unknown |
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name: score |
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value: 7.34 |
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source: |
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url: https://huggingface.co/spaces/lmsys/mt-bench |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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<img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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# Neuronx model for [Zephyr 7B β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) |
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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). |
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You can find detailed information about the base model on its [Model Card](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). |
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This model has been exported to the `neuron` format using specific `input_shapes` and `compiler` parameters detailed in the paragraphs below. |
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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. |
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## Usage on Amazon SageMaker |
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_coming soon_ |
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## Usage with 🤗 `optimum-neuron` |
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```python |
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from optimum.neuron import pipeline |
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pipe = pipeline('text-generation', 'aws-neuron/zephyr-7b-seqlen-2048-bs-4-cores-2') |
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a friendly chatbot who always responds in the style of a pirate", |
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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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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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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. |
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## Arguments passed during export |
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**input_shapes** |
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```json |
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{ |
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"batch_size": 4, |
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"sequence_length": 2048, |
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} |
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``` |
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**compiler_args** |
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```json |
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{ |
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"auto_cast_type": "fp16", |
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"num_cores": 2, |
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} |
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``` |
