# gpt2 vs macbert4csc-base-chinese: Specs, License and GPU Cost Source: https://savrn.com/models/compare/gpt2-vs-macbert4csc-base-chinese Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- | Field | [gpt2](https://savrn.com/models/gpt2) openai-community/gpt2 | [macbert4csc-base-chinese](https://savrn.com/models/macbert4csc-base-chinese) shibing624/macbert4csc-base-chinese | | --- | --- | --- | | Publisher | OpenAI community | Ming Xu (徐明) | | Task | Text generation | Text generation | | Modality | Text | Text | | Parameters, as reported | 137M parameters | 102M parameters | | Architecture | GPT2LMHeadModel | BertForMaskedLM | | Library | transformers | transformers | | Context length | Not stated | 512 tokens | | Repository size | 5.6 GB | 1.3 GB | | Artifact formats | safetensors, onnx, pytorch, tflite, jax, rust, tf | safetensors, onnx, pytorch | | License | mit | apache-2.0 | | Access | Open weights, no gate | Open weights, no gate | | Memory at 16-bit (weights and margin) | 0.3 GB | 0.2 GB | | Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 | | Memory at 4-bit (weights and margin) | 0.1 GB | 0.1 GB | | Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 | | Revision viewed | 607a30d783df | 615e6e09ef9a | | Downloads reported by the hub | 18.4M | 543.5k | | Last observed | 2026-10-22 | 2026-09-18 | An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking. ## SAVRN's Notes on gpt2 Seven formats ship in this repository: safetensors, ONNX, PyTorch, TFLite, JAX, Rust and TensorFlow. That is why 26 files add up to 5,632,417,295 bytes for a 137M-parameter text generator, and why a mirror should hold only the format your serving stack loads. It was pretrained with a causal language modeling objective on raw English text, no human labeling. At 16-bit, weights and working memory are each 0.3 GB; on the cheapest setup we list, an MI300X with 192 GB at $1.85 per hour, the model is a rounding error and you size the card for whatever runs beside it. MIT asks one thing in return for commercial use, modification and redistribution: the copyright and permission notices travel with every copy. Before committing, confirm the context window, which the listing does not state, and know that it records no reported evaluations and no per-token host price on the SAVRN Index. SAVRN Research, 2026-09-18 ## SAVRN's Notes on macbert4csc-base-chinese When Chinese text arrives with wrong characters, this is the model that corrects them before anything downstream reads it. Ming Xu built it on the MacBERT approach in arXiv:2004.13922, trained it on the shibing624/CSC data, and supports it through the pycorrector project. Hardware is a non-issue: 102M parameters, 0.2 GB of weights and 0.2 GB of memory at 16-bit. The cheapest card in our Index, one MI300X with 192 GB at $1.85 per hour, would use a tenth of one percent of its memory, so plan it as a sidecar on shared hardware. Apache 2.0 covers commercial deployment, modification and redistribution with an express patent grant, as long as notices stay intact and significant changes are stated. Check two things: the 512-token window means long documents are corrected in pieces, and the publisher used the SIGHAN2015 training set, so scores on that test set are not an external measure. SAVRN Research, 2026-09-18 ## Questions ### Which is larger, gpt2 or macbert4csc-base-chinese? gpt2 (137M parameters) is larger than macbert4csc-base-chinese (102M parameters), by the parameter counts their publishers report. ### Which is cheaper to run, gpt2 or macbert4csc-base-chinese? At 4-bit, gpt2 fits on 1x MI300X from $1.85 an hour and macbert4csc-base-chinese on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use gpt2 commercially? Yes. gpt2 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### Can I use macbert4csc-base-chinese commercially? Yes. macbert4csc-base-chinese is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Related Comparisons - [gpt2 vs SmolLM2-135M](https://savrn.com/models/compare/gpt2-vs-smollm2-135m) - [gpt2 vs SmolLM2-135M-Instruct](https://savrn.com/models/compare/gpt2-vs-smollm2-135m-instruct)