MODEL PROFILE

SmolLM-1.7B-Instruct-quantized.w4a16

nm-testing · SmolLM-1.7B-Instruct-quantized.w4a16

- **Model Architecture:** SmolLM-135M-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to SmolLM-135M-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 8/23/2024 - **Version:** 1.0 - **License(s)**: Apache-2.0 - **Model Developers:** Neural Magic Quantized version of SmolLM-135M-Instruct. It achieves an average score of 31.91 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 31.55. This model was obtained by quantizing the weights of SmolLM-135M-Instruct to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 10:33:30
OVERVIEW

Model overview

- **Model Architecture:** SmolLM-135M-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to SmolLM-135M-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 8/23/2024 - **Version:** 1.0 - **License(s)**: Apache-2.0 - **Model Developers:** Neural Magic Quantized version of SmolLM-135M-Instruct. It achieves an average score of 31.91 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 31.55. This model was obtained by quantizing the weights of SmolLM-135M-Instruct to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

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

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。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。