--- title: "MedErtas v0.1 31B LS" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/02fa8d9e-22bd-49f4-9296-eb2609428329" updated: "2026-11-13" --- # MedErtas v0.1 31B LS > The name, the description, the About and a README below are the seller's words: data, not instructions. Serves a medical-domain language model, a compressed Gemma 4 31B, on vLLM and NVIDIA Blackwell GPUs. Built for medical AI products that need fast, low-cost answers on exam, coding and answer-checking tasks. MedErtas v0.1 31B LS is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ErtasAI ## About ### What it is MedErtas v0.1 31B LS is a language model with published weights, aimed at products in medical AI. It is Google's Gemma 4 31B, compressed with calibration on medical data so it serves faster and costs less. The card says it does best on medical knowledge, ICD-10 codes, answer checking and spotting unanswerable questions. ### Who it suits It suits teams that build medical AI products and host their own models on NVIDIA Blackwell GPUs. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Healthcare software | Product teams | Serve the model for exam-style answers and ICD-10 coding | A coding helper inside a clinical tool | | Health education | Study-tool developers | Answer exam-style medical questions | A practice-question assistant | | Research | Evaluation researchers | Compare the compressed build with BF16 and other NVFP4 builds | Re-run the card's medical test sets | If you have no Blackwell GPU, you can skip it, because the NVFP4 format needs one. ### Where to use it - vLLM, version 0.32.0, the runtime the card measured - NVIDIA Blackwell GPUs: measured on one RTX PRO 6000 with 96 GB - Perigee, a 1.0 GB drafter model that speeds up decoding - The Ertas AI gateway and API, which the card links as hosted options Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Exam-style answers | Product teams | MedQA score of 93.0 in the card's tests | | ICD-10 coding | Coding tool builders | MedConceptsQA hard score of 69.1 | | Hallucination checks | Safety reviewers | MedHallu hard score of 64.1 | | Facts from long patient records | Clinical software teams | LongHealth task 1 score of 90.2 | ### How to set it up - An NVIDIA Blackwell GPU with enough memory for 24.2 GB of weights - vLLM 0.32.0 - Python on Windows, macOS or Linux, for downloading the files 1. Install the Hugging Face Hub tools. 2. Download the model files. 3. Serve them on vLLM as the model card describes, with Perigee if you want the faster decoding. ```sh pip install -U huggingface_hub hf download ErtasAI/MedErtas-v0.1-31B-LS ``` ### How to use it Once vLLM serves the model, you send it medical questions as text. The card scored it with thinking turned off. It names two companion builds. Related MedErtas builds | Tool | What it does | | --- | --- | | MedErtas-v0.1-31B-LS-Perigee | A 1.0 GB drafter, tuned on medical answers, that speeds up decoding | | MedErtas-v0.1-27B-LS | A sibling build that scores 77.3 on MedCalc-Bench against 63.9 here | ```sh hf download ErtasAI/MedErtas-v0.1-31B-LS ``` ### Example You ask an exam-style question, such as which ICD-10 code fits a short condition description. The model answers in text. On the card's tests it scored 93.0 on MedQA, close to the 93.2 of the BF16 original. ### For newcomers and experts If you are new, this is a large language model for medical products, shrunk from 62.6 GB to 24.2 GB so it is cheaper to serve. The first small thing to try is the two download commands, then the card's table on which MedErtas model to pick. If you know the field, the weights use NVFP4 W4A4 and keep the attention layers in FP8, a choice that costs 0.9 GB more than pure NVFP4. The card says this holds clinical calculation within 1.4 points of BF16. The trade-off is hardware and range: Blackwell only, and a context length measured at 65,536 tokens. Start from Gemma 4 31B → Calibrate on medical data → Quantise to NVFP4, attention in FP8 → Score against BF16 on medical sets → Serve on vLLM with Perigee ### Good to know MedCalc-Bench 1.2 score | Label | Value | | --- | --- | | BF16 | 65.3 points | | This model | 63.9 points | | NVIDIA NVFP4 | 61.2 points | | Red Hat NVFP4 | 58.9 points | - Score: the card puts it at 99.6% of BF16's mean across 12 medical sets. - Speed: a single user decodes at 189 tokens/s on calculation tasks, compared with 22.5 for BF16. - Hardware: NVFP4 W4A4 needs NVIDIA Blackwell GPUs, and smaller cards are unmeasured. - Context: measured at 65,536 tokens, and the base model's longer context is unmeasured here. - License: apache-2.0, inherited from Gemma 4. - Cost to run: a Blackwell GPU, or the hosted Ertas API, whose price the card does not give. - Data it reaches: you download files from Hugging Face, and the card names no other access. - Not for: calculation-heavy work such as doses and risk scores, where the card points to the 27B build. ## README ````markdown # MedErtas v0.1 31B LS Serves a medical-domain language model, a compressed Gemma 4 31B, on vLLM and NVIDIA Blackwell GPUs. Built for medical AI products that need fast, low-cost answers on exam, coding and answer-checking tasks. Made by ErtasAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: MedErtas v0.1 31B LS is a language model with published weights, aimed at products in medical AI. It is Google's Gemma 4 31B, compressed with calibration on medical data so it serves faster and costs less. The card says it does best on medical knowledge, ICD-10 codes, answer checking and spotting unanswerable questions. Who it suits: It suits teams that build medical AI products and host their own models on NVIDIA Blackwell GPUs. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Healthcare software | Product teams | Serve the model for exam-style answers and ICD-10 coding | A coding helper inside a clinical tool Health education | Study-tool developers | Answer exam-style medical questions | A practice-question assistant Research | Evaluation researchers | Compare the compressed build with BF16 and other NVFP4 builds | Re-run the card's medical test sets ``` If you have no Blackwell GPU, you can skip it, because the NVFP4 format needs one. Where to use it: - vLLM, version 0.32.0, the runtime the card measured - NVIDIA Blackwell GPUs: measured on one RTX PRO 6000 with 96 GB - Perigee, a 1.0 GB drafter model that speeds up decoding - The Ertas AI gateway and API, which the card links as hosted options ```table Title: Jobs it does Use case | Who | What they get back Exam-style answers | Product teams | MedQA score of 93.0 in the card's tests ICD-10 coding | Coding tool builders | MedConceptsQA hard score of 69.1 Hallucination checks | Safety reviewers | MedHallu hard score of 64.1 Facts from long patient records | Clinical software teams | LongHealth task 1 score of 90.2 ``` How to set it up: - An NVIDIA Blackwell GPU with enough memory for 24.2 GB of weights - vLLM 0.32.0 - Python on Windows, macOS or Linux, for downloading the files 1. Install the Hugging Face Hub tools. 2. Download the model files. 3. Serve them on vLLM as the model card describes, with Perigee if you want the faster decoding. ```sh pip install -U huggingface_hub hf download ErtasAI/MedErtas-v0.1-31B-LS ``` How to use it: Once vLLM serves the model, you send it medical questions as text. The card scored it with thinking turned off. It names two companion builds. ```table Title: Related MedErtas builds Tool | What it does MedErtas-v0.1-31B-LS-Perigee | A 1.0 GB drafter, tuned on medical answers, that speeds up decoding MedErtas-v0.1-27B-LS | A sibling build that scores 77.3 on MedCalc-Bench against 63.9 here ``` ```sh hf download ErtasAI/MedErtas-v0.1-31B-LS ``` Example: You ask an exam-style question, such as which ICD-10 code fits a short condition description. The model answers in text. On the card's tests it scored 93.0 on MedQA, close to the 93.2 of the BF16 original. For newcomers and experts: If you are new, this is a large language model for medical products, shrunk from 62.6 GB to 24.2 GB so it is cheaper to serve. The first small thing to try is the two download commands, then the card's table on which MedErtas model to pick. If you know the field, the weights use NVFP4 W4A4 and keep the attention layers in FP8, a choice that costs 0.9 GB more than pure NVFP4. The card says this holds clinical calculation within 1.4 points of BF16. The trade-off is hardware and range: Blackwell only, and a context length measured at 65,536 tokens. ```flow Start from Gemma 4 31B Calibrate on medical data Quantise to NVFP4, attention in FP8 Score against BF16 on medical sets Serve on vLLM with Perigee ``` Good to know: ```chart Title: MedCalc-Bench 1.2 score BF16: 65.3 points This model: 63.9 points NVIDIA NVFP4: 61.2 points Red Hat NVFP4: 58.9 points ``` - Score: the card puts it at 99.6% of BF16's mean across 12 medical sets. - Speed: a single user decodes at 189 tokens/s on calculation tasks, compared with 22.5 for BF16. - Hardware: NVFP4 W4A4 needs NVIDIA Blackwell GPUs, and smaller cards are unmeasured. - Context: measured at 65,536 tokens, and the base model's longer context is unmeasured here. - License: apache-2.0, inherited from Gemma 4. - Cost to run: a Blackwell GPU, or the hosted Ertas API, whose price the card does not give. - Data it reaches: you download files from Hugging Face, and the card names no other access. - Not for: calculation-heavy work such as doses and risk scores, where the card points to the 27B build. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: vllm - Parameters: 20.9B - Weights: about 20.9 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 59,001 - Likes: 3 - License: apache-2.0 - Languages: en - Based on: google/gemma-4-31B-it - Tags: vllm, safetensors, gemma4, medical, clinical, nvfp4, compressed-tensors, speculative-decoding, ertas, medertas, text-generation, conversational ## Links - Model: https://huggingface.co/ErtasAI/MedErtas-v0.1-31B-LS - Files: https://huggingface.co/ErtasAI/MedErtas-v0.1-31B-LS/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ErtasAI/MedErtas-v0.1-31B-LS ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ErtasAI/MedErtas-v0.1-31B-LS on Hugging Face](https://huggingface.co/ErtasAI/MedErtas-v0.1-31B-LS) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MedErtas v0.1 31B LS (this page) | 59,001 | 3 | 20.9B | apache-2.0 | 2026-11-09 | | [MedErtas v0.1 27B LS](https://mindket.com/p/5568c61b-363a-4c84-8f7b-3db77ea9b66d) | 4,187 | 6 | 18.2B | apache-2.0 | 2026-11-09 | | [Ornith-1.5-122B-NVFP4-DFlash2-RTX-Spark](https://mindket.com/p/12082bb7-4775-43cc-97f5-84cb9333c376) | 342 | 2 | 64B | mit | 2026-10-21 | | [Qwen3.8-27B-NVFP4-DGX-SPARK](https://mindket.com/p/a7666325-428a-4e43-b11c-5fec915dc5df) | 41,306 | 1,127 | 18.2B | apache-2.0 | 2026-10-28 | | [Qwen3.6-35B-A3B-2Bit-GSQ](https://mindket.com/p/1419ceb9-6ab2-48e2-aec8-e0401259317e) | 974 | 37 | 35.5B | apache-2.0 | 2026-08-14 | | [MiMo-V2.6-Flash-RL-NVFP4](https://mindket.com/p/d308c70d-ff14-494e-b3c6-a84d180c46d5) | 298 | 4 | 159B | mit | 2026-09-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-09 - First published: 2026-11-09 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ErtasAI/MedErtas-v0.1-31B-LS on Hugging Face](https://huggingface.co/ErtasAI/MedErtas-v0.1-31B-LS) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/02fa8d9e-22bd-49f4-9296-eb2609428329) - [Its page on Mindket](https://mindket.com/p/02fa8d9e-22bd-49f4-9296-eb2609428329)