stablelm-2-zephyr-1

Model Description

Stable LM 2 Zephyr 1.6B is a 1.6 billion parameter instruction tuned language model inspired by HugginFaceH4's Zephyr 7B training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO).

Usage

StableLM 2 Zephyr 1.6B uses the following instruction format:

Which famous math number begins with 1.6 ...?
The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio

This format is also available through the tokenizer's apply_chat_template method:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b')
model = AutoModelForCausalLM.from_pretrained(
    'stabilityai/stablelm-2-zephyr-1_6b',
    device_map="auto"
)

prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}]
inputs = tokenizer.apply_chat_template(
    prompt,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to(model.device),
    max_new_tokens=1024,
    temperature=0.5,
    do_sample=True
)

print(tokenizer.decode(tokens[0], skip_special_tokens=False))

Model Details

• Developed by: Stability AI
• Model type: StableLM 2 Zephyr 1.6B model is an auto-regressive language model based on the transformer decoder architecture.
• Language(s): English
• Paper: Stable LM 2 1.6B Technical Report
• Library: Alignment Handbook
• Finetuned from model: https://huggingface.co/stabilityai/stablelm-2-1_6b
• License: StabilityAI Non-Commercial Research Community License. If you want to use this model for your commercial products or purposes, please contact us here to learn more.
• 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:

1. SFT Datasets

• HuggingFaceH4/ultrachat_200k
• meta-math/MetaMathQA
• WizardLM/WizardLM_evol_instruct_V2_196k
• Open-Orca/SlimOrca
• openchat/openchat_sharegpt4_dataset
• LDJnr/Capybara
• hkust-nlp/deita-10k-v0

2. Preference Datasets:

• allenai/ultrafeedback_binarized_cleaned
• Intel/orca_dpo_pairs

Performance

MT-Bench

OpenLLM Leaderboard

Training Infrastructure

• Hardware: StableLM 2 Zephyr 1.6B 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 in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about safety and limitations below.

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