metadata

datasets:
  - HuggingFaceH4/ultrachat_200k
  - HuggingFaceH4/ultrafeedback_binarized
  - meta-math/MetaMathQA
  - WizardLM/WizardLM_evol_instruct_V2_196k
language:
  - en
tags:
  - causal-lm
extra_gated_fields:
  Name: text
  Email: text
  Country: text
  Organization or Affiliation: text
  I ALLOW Stability AI to email me about new model releases: checkbox

`Stable Zephyr 3B`

Model Description

Stable Zephyr 3B is a 3 billion parameter instruction tuned inspired by HugginFaceH4's Zephyr 7B training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using Direct Preference Optimization (DPO), evaluation for this model based on MT Bench and Alpaca Benchmark

Usage

Get started generating text with Stable Zephyr 3B by using the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-zephyr-3b-dpo")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stable-zephyr-3b-dpo",
  trust_remote_code=True,
  torch_dtype="auto",
)
model.cuda()
prompt = "<|user|>\nIn the field of quantum physics, what is superposition, and how does it relate to the phenomenon of quantum entanglement?<|endoftext|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
tokens = model.generate(
  **inputs,
  max_new_tokens=1024,
  temperature=0.7,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Model Details

Developed by: Stability AI
Model type: Stable Zephyr 3B models are auto-regressive language models based on the transformer decoder architecture.
Language(s): English
Library: Alignment Handbook
Finetuned from model: stabilityai/stablelm-3b-4e1t
License: TBD
Contact: For questions and comments about the model, please email lm@stability.ai

Training Dataset

The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub:

HuggingFaceH4/ultrachat_200k
HuggingFaceH4/ultrafeedback_binarized
meta-math/MetaMathQA
Capybara
Instruct Code Dataset (Internal)
Wizard Dataset

Training Procedure

Performance

Model	Size	Alignment	MT-Bench (score)	AlpacaEval (win rate %)
Stable Zephyr 3B 🪁	3B	DPO	6.86	75.19
Stable Zephyr (SFT only)	3B	SFT	7.12	71.15
MPT-Chat	7B	dSFT	5.42	-
Xwin-LMv0.1	7B	dPPO	6.19	87.83
Mistral-Instructv0.1	7B	-	6.84	-
Zephyr-7b-α	7B	dDPO	6.88	-
Zephyr-7b-β	7B	dDPO	7.34	90.60
Falcon-Instruct	40B	dSFT	5.17	45.71
Guanaco	65B	SFT	6.41	71.80
Llama2-Chat	70B	RLHF	6.86	92.66
Vicuna v1.3	33B	dSFT	7.12	88.99
WizardLM v1.0	70B	dSFT	7.71	-
Xwin-LM v0.1	70B	dPPO	-	95.57
GPT-3.5-turbo	-	RLHF	7.94	89.37
Claude 2	-	RLHF	8.06	91.36
GPT-4	-	RLHF	8.99	95.28

Training Infrastructure

Hardware: Stable Zephyr 3B was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes.
Code Base: We use our internal script for SFT steps and used HuggingFace Alignment Handbook script for DPO training.

Use and Limitations

Intended Use

The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications.

Limitations and Bias

As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.