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qwen35-9b-medical is an Ollama/GGUF medical assistant profile based on Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2-GGUF, distributed locally through the Ollama model kwangsuklee/Qwen3.5-9B.Q4KM-Claude-4.6-Opus-Reasoning-Distilled-v2.

# Instruction For more information, visit our GitHub repository: https://github.com/medfound/medfound

This repository contains GGUF files for gemma4-12b-bioinfo, a fine-tuned Gemma 4 12B model for bioinformatics and computational biology.

gemma4-12b-bioinfo is a fine-tuned Gemma 4 12B instruction model for bioinformatics, genomics, and computational biology question answering.

modelid = "DuanYi/R3LMHepG2"

*GenerRNA is a generative pre-trained language model for de novo RNA sequence design. It is a Transformer (decoder-only, GPT-style) model that learns the "language" of RNA from millions of natural sequences and can generate novel, realistic RNA sequences without any structural input, functional…

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Aryabhata 2 is a reasoning-focused language model developed by PhysicsWallah for competitive STEM examinations (JEE, NEET). It is obtained by post-training GPT-OSS-20B via reinforcement learning on a curated curriculum of Physics, Chemistry, Mathematics, and General Reasoning questions — achieving…

> VIDRAFT FINAL-Bench — chemistry-specialized 218B MoE, served via the DELPHI 5-Phase inference cascade.

HealthJudge is a domain-adapted helpfulness evaluator for health-related Community Notes. It is designed to judge whether a note provides helpful context for a potentially misleading social-media post, following the Community Notes helpfulness criteria.

## Model Description ProtGPT3-112M is a single-sequence autoregressive protein language model for protein sequence generation. It is the smallest model in the ProtGPT3 family, an open-source suite of promptable and aligned protein language models ranging from 112M to 10B parameters.

ProtGPT3-10B is a single-sequence autoregressive protein language model for protein sequence generation. It is the largest model in the ProtGPT3 family, an open-source suite of promptable and aligned protein language models ranging from 112M to 10B parameters.

!Protein-ligand interaction header

GGUF quantizations of HuggingFaceBio/Carbon-3B — a generative DNA foundation model — for efficient inference with llama.cpp.

A HuggingFace-compatible repackaging of PlasmidGPT (Shao, 2024) — a GPT-2-style decoder pretrained on 153k engineered plasmid sequences from Addgene. Loadable with standard AutoModelForCausalLM and AutoTokenizer. Used as the base for PlasmidGPT-SFT and PlasmidGPT-GRPO.

Target-Conditioned Molecular Ideation Model for Drug Discovery Research

Genos-m is a foundation model for human-associated microbial genomes. It is trained to model microbial DNA sequences at single-nucleotide resolution and supports ultra-long genomic contexts up to one million tokens.

Qwen3-8B-syco_med-gated-attention-FT is a plug-and-play gated attention weight released for AI safety research.

LoRA fine-tune of Qwen3.5-9B on synthetic clinical triage Q&A pairs generated from PubMed Central open-access papers. The model is specialized for emergency-medicine decision-making: triaging patients, applying clinical decision rules, and generating protocol-grounded triage recommendations.

Base model: google/gemma-4-26b-it Architecture: MoE — 26B total / ≈4B active parameters (1 shared expert + 8 routed from a pool of 128 per MoE layer, 30 MoE layers) Method: Activation-directed expert surgery — 128 → 64 experts per layer (50% reduction) Quantization: Q4KM (≈9.7 GB on disk) Tags:…

Base model: google/gemma-4-26b-it Architecture: MoE — 26B total / ≈4B active parameters (1 shared expert + 8 routed from a pool of 128 per MoE layer, 30 MoE layers) Method: Activation-directed expert surgery — 128 → 64 experts per layer (50% reduction) Quantization: Q4KM (≈9.7 GB on disk) Tags:…

Base model: google/gemma-4-26b-it Architecture: MoE — 26B total / ≈4B active parameters (1 shared expert + 8 routed from a pool of 128 per MoE layer, 30 MoE layers) Method: Activation-directed expert surgery — 128 → 64 experts per layer (50% reduction) Quantization: Q4KM (≈9.7 GB on disk) Tags:…

Gemma 4 E2B fine-tuned on 225K drug–target pairs for novel small-molecule generation.

MedPsy-4B is a state-of-the-art, text-only medical and healthcare language model purpose-built for edge deployment. Built on top of Qwen3-4B-Thinking-2507 and post-trained with a multi-stage pipeline (supervised fine-tuning + reinforcement learning) on curated medical data, it surpasses models…

MedPsy-1.7B is a state-of-the-art, text-only medical and healthcare language model purpose-built for edge and smartphone deployment. Built on top of Qwen3-1.7B (operated in thinking mode, i.e. with enable_thinking=True) and post-trained with a multi-stage pipeline (supervised fine-tuning +…

InstaNovo-P is a specialized transformer-based model for de novo peptide sequencing from phosphoproteomics mass spectrometry data. This model is specifically trained and optimized for identifying phosphorylated peptides and their modification sites.

# InstaNovo: De novo Peptide Sequencing Model ## Model Description

# InstaNovo: De novo Peptide Sequencing Model ## Model Description

A domain-optimized reasoning model built on DeepSeek-R1-Distill-Qwen-32B, refined through a multi-stage pipeline of GPTQ quantization-aware training and QLoRA fine-tuning. Achieves 84% on MedQA — within 4 points of GPT-4o — in a ~20GB package that fits on a single L40/L40s GPU.

Duchifat-2.3-Instruct is a state-of-the-art, instruction-tuned Large Language Model developed by TopAI. As the flagship of the Duchifat series, this model represents a fundamental breakthrough in how Hebrew is processed, reasoned, and generated in the LLM era.

Fine-tuned version of google/gemma-4-E4B-it across three professional domains — Medical, Legal, and Finance — using QLoRA (4-bit NF4) with Optuna-tuned hyperparameters, trained on Kaggle T4 GPU.

L1 (Learning Unit 1) is the first language model from Lunit and Lunit Consortium, purpose-built for the medical domain. Derived from Gravity-16B-A3B-Base, L1 is designed for clinical reasoning and decision support.

# or·a·cle /ˈôrəkəl/ — a source of wise counsel; one who provides authoritative knowledge. From Latin ōrāculum, meaning divine announcement. In computer science, an oracle is a black box that always returns the correct answer — you don't ask it how it knows, you ask and it answers.

EVA is a generative foundation model for universal RNA modeling and design, trained on OpenRNA v1 — a curated atlas of 114 million full-length RNA sequences spanning all domains of life.

ClinicDx V1 is a fine-tuned multimodal clinical decision support (CDS) model based on google/medgemma-4b-it. It is trained to generate structured, evidence-grounded clinical assessments from patient presentations, integrating a retrieval-augmented knowledge base (KB) pipeline and an audio input…

Vortex Scientific is a from-scratch AI model family designed for deep scientific reasoning. Built from the ground up with a novel hybrid state-space + attention architecture, optimized for consumer laptop hardware (Apple Silicon MacBooks and Nvidia 4060 laptop GPUs).

This repository contains the model presented in D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 31% better perplexity than standard knowledge distillation at 3.8x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 54% better perplexity than standard knowledge distillation at 9.4x compression.

A compact protein language model distilled from ProtGPT2 using complementary-regularizer distillation---a method that combines uncertainty-aware position weighting with calibration-aware label smoothing to achieve 87% better perplexity than standard knowledge distillation at 20x compression.

From Inquiry to Decision: Building Trustworthy Medical AI

WiNGPT 是一个基于GPT的医疗垂直领域大模型，旨在将专业的医学知识、医疗信息、数据融会贯通，为医疗行业提供智能化的医疗问答、诊断支持和医学知识等信息服务，提高诊疗效率和医疗服务质量。

While large language models (LLMs) have achieved impressive progress, their application in scientific domains such as chemistry remains hindered by shallow domain understanding and limited reasoning capabilities. In this work, we focus on the specific field of chemistry and develop a Chemical…

ChemDFM-v2.0 is the latest non-thinking model of ChemDFM, the pioneering open-sourced dialogue foundation model for Chemistry and molecule science.

> [!NOTE] > Inspired by the thought of: what if you could speak to an offline medical assistant that doesn't decline to answer some of your questions?

The MediPhi Model Collection comprises 7 small language models of 3.8B parameters from the base model Phi-3.5-mini-instruct specialized in the medical and clinical domains. The collection is designed in a modular fashion. Five MediPhi experts are fine-tuned on various medical corpora (i.e.