# Standard Thinking: Open-Weight Models and Datasets Source: https://savrn.com/model-publishers/standardthinking Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Models Model · Text generation ### [StandardOne-3B-GGUF](https://savrn.com/models/standardone-3b-gguf) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) GGUF builds of Standard One 3B (Ministral 3 3B text + Pixtral vision tower, mistral3 architecture) for use with llama.cpp. Most quant levels below (marked "imatrix") were built with an importance matrix calibrated on 1,512 prompts sampled from our own training rows (see "Importance-matrix calibration" below), which recovers some of the accuracy quantization would otherwise lose. Q80 and BF16 don't need one. SHA256 checksums: SHA256SUMS. Source revisions, conversion tool version and full validation Q4KM note: this is the imatrix-calibrated version, not a plain quantization. We generated both and chose whichever scored higher on the mean of 10 held-out and public-dataset decision suites (no… Open weights apache-2.0 gguf [View model](https://savrn.com/models/standardone-3b-gguf) Model · Text generation ### [StandardOne-8B-GGUF](https://savrn.com/models/standardone-8b-gguf) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) GGUF builds of Standard One 8B (Ministral 3 8B text + Pixtral vision tower, mistral3 architecture) for use with llama.cpp. Most quant levels below (marked "imatrix") were built with an importance matrix calibrated on 1,512 prompts sampled from our own training rows (see "Importance-matrix calibration" below), which recovers some of the accuracy quantization would otherwise lose. Q80 and BF16 don't need one. SHA256 checksums: SHA256SUMS. Source revisions, conversion tool version and full validation Q4KM note: this is the imatrix-calibrated version, not a plain quantization. We generated both and chose whichever scored higher on the mean of 10 held-out and public-dataset decision suites (no… Open weights apache-2.0 gguf [View model](https://savrn.com/models/standardone-8b-gguf) Model · Text generation ### [StandardOne-8B](https://savrn.com/models/standardone-8b) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the merged BF16 8B checkpoint and the server code. In the reported served evaluations, 8B scores higher than 3B on the public standard and hard tiers; 3B has a lower median latency on the measured short-request profile. See Benchmarks for the measurement conditions and limitations. The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026. - Send a state and a bounded rubric to receive probabilities for the… Open weights apache-2.0 8.9B parameters 262,144 tokens transformers [View model](https://savrn.com/models/standardone-8b) Model · Text generation ### [StandardOne-3B](https://savrn.com/models/standardone-3b) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the merged BF16 3B checkpoint; the server code is in StandardOne-8B. In the reported served evaluations, 3B has a lower median latency on the measured short-request profile; 8B scores higher on the public standard and hard tiers. See Benchmarks for the measurement conditions and limitations. The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026. - Send a state and a bounded rubric to receive probabilities for… Open weights apache-2.0 3.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/standardone-3b) Model · Text generation ### [StandardOne-3B-FP8](https://savrn.com/models/standardone-3b-fp8) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) StandardOne-3B-FP8 is an FP8 (compressed-tensors, float8e4m3 weights, dynamic per-token activations) quantization of the released StandardOne-3B decision model. The language-model linear projections (q/k/v/o, gate/up/down) are quantized per-channel FP8 E4M3 with dynamic FP8 activations (llm-compressor's data-free FP8DYNAMIC recipe, no calibration data required); the vision tower, multi-modal projector, embeddings and lmhead are left unquantized in BF16. It was produced from source revision 68dafd17ead9b8cf6f85c4f08f7f2f3a1e7b9e5c of StandardOne-3B on 2026-09-25 using llm-compressor 0.14.0 (torch 2.14.0, transformers 5.17.0, compressed-tensors 0.19.0); results below. Served through SGLang… Open weights apache-2.0 3.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/standardone-3b-fp8) Model · Text generation ### [StandardOne-8B-FP8](https://savrn.com/models/standardone-8b-fp8) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) StandardOne-8B-FP8 is an FP8 (compressed-tensors, float8e4m3 weights, dynamic per-token activations) quantization of the released StandardOne-8B decision model. The language-model linear projections (q/k/v/o, gate/up/down) are quantized per-channel FP8 E4M3 with dynamic FP8 activations (llm-compressor's data-free FP8DYNAMIC recipe, no calibration data required); the vision tower, multi-modal projector, embeddings and lmhead are left unquantized in BF16. It was produced from source revision e88423700bb5ab9b2f50e176cf19825914345272 of StandardOne-8B on 2026-09-25 using llm-compressor 0.14.0 (torch 2.14.0, transformers 5.17.0, compressed-tensors 0.19.0); results below. Served through SGLang… Open weights apache-2.0 8.9B parameters 262,144 tokens transformers [View model](https://savrn.com/models/standardone-8b-fp8) Model · Text generation ### [StandardOne-3B-LoRA](https://savrn.com/models/standardone-3b-lora) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the 3B LoRA adapter and a merge recipe; serving requires a merged checkpoint and the server code. In the reported served evaluations, 3B has a lower median latency on the measured short-request profile; 8B scores higher on the public standard and hard tiers. See Benchmarks for the measurement conditions and limitations. The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026. - Send a state and a bounded rubric… Open weights apache-2.0 peft [View model](https://savrn.com/models/standardone-3b-lora) Model · Text generation ### [StandardOne-8B-LoRA](https://savrn.com/models/standardone-8b-lora) [Standard Thinking](https://savrn.com/model-publishers/standardthinking) Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the 8B LoRA adapter and a merge recipe; serving requires a merged checkpoint and the server code. In the reported served evaluations, 8B scores higher than 3B on the public standard and hard tiers; 3B has a lower median latency on the measured short-request profile. See Benchmarks for the measurement conditions and limitations. The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026. - Send a state and a bounded… Open weights apache-2.0 peft [View model](https://savrn.com/models/standardone-8b-lora) ## Explore More - [All model publishers](https://savrn.com/model-publishers) - [The model directory](https://savrn.com/models) - [The dataset directory](https://savrn.com/datasets) ## Source - Listed from their public repositories, read 2026-09-25. - [Hugging Face profile](https://huggingface.co/StandardThinking) - [How the hub is built](https://savrn.com/model-hub/methodology)