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- **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%.
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
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