MODEL PROFILE

stablelm-2-zephyr-1_6b

Stability AI · stablelm-2-zephyr-1_6b

`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). 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 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.

Text generationlicense:other
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 19:13:42
OVERVIEW

Model overview

`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). 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 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.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。