metadata
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|>
You are a pirate chatbot who always responds with Arr!</s>
<|user|>
There's a llama on my lawn, how can I get rid of him?</s>
<|assistant|>
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:
- 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
- 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
- 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: 9.66243708053691
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
- 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
- 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
- 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
- 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: 77.7426992896606
source:
name: Open LLM Leaderboard
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
- task:
type: text-generation
name: Text Generation
dataset:
name: AlpacaEval
type: tatsu-lab/alpaca_eval
metrics:
- type: unknown
name: win rate
value: 0.906
source:
url: https://tatsu-lab.github.io/alpaca_eval/
- 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
Neuronx model for Zephyr 7B β
This repository contains AWS Inferentia2 and neuronx compatible checkpoints for HuggingFaceH4/zephyr-7b-beta.
You can find detailed information about the base model on its Model Card.
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 for an explanation of these parameters.
Usage on Amazon SageMaker
coming soon
Usage with 🤗 optimum-neuron
from optimum.neuron import pipeline
pipe = 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
{
"batch_size": 4,
"sequence_length": 2048,
}
compiler_args
{
"auto_cast_type": "fp16",
"num_cores": 2,
}