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medgemma-4b-it
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in two variants: a 4B multimodal version and a 27B text-only version.
MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.
MedGemma 27B has been trained exclusively on medical text and optimized for inference-time computation. MedGemma 27B is only available as an instruction-tuned model.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These include both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended Use section below for more details.
Repository: localaiLicense: health-ai-developer-foundations
medgemma-27b-text-it
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in two variants: a 4B multimodal version and a 27B text-only version.
MedGemma 4B utilizes a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Its LLM component is trained on a diverse set of medical data, including radiology images, histopathology patches, ophthalmology images, and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.
MedGemma 27B has been trained exclusively on medical text and optimized for inference-time computation. MedGemma 27B is only available as an instruction-tuned model.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These include both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended Use section below for more details.
Repository: localaiLicense: health-ai-developer-foundations
google_medgemma-4b-it
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in three variants: a 4B multimodal version and 27B text-only and multimodal versions.
Both MedGemma multimodal versions utilize a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Their LLM components are trained on a diverse set of medical data, including medical text, medical question-answer pairs, FHIR-based electronic health record data (27B multimodal only), radiology images, histopathology patches, ophthalmology images, and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.
MedGemma 27B multimodal has pre-training on medical image, medical record and medical record comprehension tasks. MedGemma 27B text-only has been trained exclusively on medical text. Both models have been optimized for inference-time computation on medical reasoning. This means it has slightly higher performance on some text benchmarks than MedGemma 27B multimodal. Users who want to work with a single model for both medical text, medical record and medical image tasks are better suited for MedGemma 27B multimodal. Those that only need text use-cases may be better served with the text-only variant. Both MedGemma 27B variants are only available in instruction-tuned versions.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These evaluations are based on both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended Use section below for more details.
MedGemma is optimized for medical applications that involve a text generation component. For medical image-based applications that do not involve text generation, such as data-efficient classification, zero-shot classification, or content-based or semantic image retrieval, the MedSigLIP image encoder is recommended. MedSigLIP is based on the same image encoder that powers MedGemma.
Repository: localaiLicense: health-ai-developer-foundations
google_medgemma-27b-it
MedGemma is a collection of Gemma 3 variants that are trained for performance on medical text and image comprehension. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma currently comes in three variants: a 4B multimodal version and 27B text-only and multimodal versions.
Both MedGemma multimodal versions utilize a SigLIP image encoder that has been specifically pre-trained on a variety of de-identified medical data, including chest X-rays, dermatology images, ophthalmology images, and histopathology slides. Their LLM components are trained on a diverse set of medical data, including medical text, medical question-answer pairs, FHIR-based electronic health record data (27B multimodal only), radiology images, histopathology patches, ophthalmology images, and dermatology images.
MedGemma 4B is available in both pre-trained (suffix: -pt) and instruction-tuned (suffix -it) versions. The instruction-tuned version is a better starting point for most applications. The pre-trained version is available for those who want to experiment more deeply with the models.
MedGemma 27B multimodal has pre-training on medical image, medical record and medical record comprehension tasks. MedGemma 27B text-only has been trained exclusively on medical text. Both models have been optimized for inference-time computation on medical reasoning. This means it has slightly higher performance on some text benchmarks than MedGemma 27B multimodal. Users who want to work with a single model for both medical text, medical record and medical image tasks are better suited for MedGemma 27B multimodal. Those that only need text use-cases may be better served with the text-only variant. Both MedGemma 27B variants are only available in instruction-tuned versions.
MedGemma variants have been evaluated on a range of clinically relevant benchmarks to illustrate their baseline performance. These evaluations are based on both open benchmark datasets and curated datasets. Developers can fine-tune MedGemma variants for improved performance. Consult the Intended use section below for more details.
MedGemma is optimized for medical applications that involve a text generation component. For medical image-based applications that do not involve text generation, such as data-efficient classification, zero-shot classification, or content-based or semantic image retrieval, the MedSigLIP image encoder is recommended. MedSigLIP is based on the same image encoder that powers MedGemma.
Repository: localaiLicense: health-ai-developer-foundations
doctoraifinetune-3.1-8b-i1
This is a fine-tuned version of the Meta-Llama-3.1-8B-bnb-4bit model, specifically adapted for the medical field. It has been trained using a dataset that provides extensive information on diseases, symptoms, and treatments, making it ideal for AI-powered healthcare tools such as medical chatbots, virtual assistants, and diagnostic support systems.
Key Features
Disease Diagnosis: Accurately identifies diseases based on symptoms provided by the user.
Symptom Analysis: Breaks down and interprets symptoms to provide a comprehensive medical overview.
Treatment Recommendations: Suggests treatments and remedies according to medical conditions.
Dataset
The model is fine-tuned on 2000 rows from a dataset consisting of 272k rows. This dataset includes rich information about diseases, symptoms, and their corresponding treatments. The model is continuously being updated and will be further trained on the remaining data in future releases to improve accuracy and capabilities.
Repository: localaiLicense: apache-2.0
openvino-llama3-aloe
Aloe is a healthcare-focused large language model based on Meta Llama 3 8B, optimized for OpenVINO inference with int8 quantization. It is instruction-tuned for medical and ethical reasoning tasks, offering competitive performance on healthcare QA datasets.
Repository: localaiLicense: cc-by-nc-4.0
financial-gpt-oss-20b-q8-i1
### **Financial GPT-OSS 20B (Base Model)**
**Model Type:** Causal Language Model (Fine-tuned for Financial Analysis)
**Architecture:** Mixture of Experts (MoE) – 20B parameters, 32 experts (4 active per token)
**Base Model:** `unsloth/gpt-oss-20b-unsloth-bnb-4bit`
**Fine-tuned With:** LoRA (Low-Rank Adaptation) on financial conversation data
**Training Data:** 22,250 financial dialogue pairs covering stocks (AAPL, NVDA, TSLA, etc.), technical analysis, risk assessment, and trading signals
**Context Length:** 131,072 tokens
**Quantization:** Q8_0 GGUF (for efficient inference)
**License:** Apache 2.0
**Key Features:**
- Specialized in financial market analysis: technical indicators (RSI, MACD), risk assessments, trading signals, and price forecasts
- Handles complex financial queries with structured, actionable insights
- Designed for real-time use with low-latency inference (GGUF format)
- Supports S&P 500 stocks and major asset classes across tech, healthcare, energy, and finance sectors
**Use Case:** Ideal for traders, analysts, and developers building financial AI tools. Use with caution—**not financial advice**.
**Citation:**
```bibtex
@misc{financial-gpt-oss-20b-q8,
title={Financial GPT-OSS 20B Q8: Fine-tuned Financial Analysis Model},
author={beenyb},
year={2025},
publisher={Hugging Face Hub},
url={https://huggingface.co/beenyb/financial-gpt-oss-20b-q8}
}
```
Repository: localaiLicense: apache-2.0