Text Generation
PEFT
Safetensors
medical
radiology
diagnosis
lora
unsloth
gpt-oss
eurorad
conversational
Instructions to use omareng/on-device-LLM-gpt-oss-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use omareng/on-device-LLM-gpt-oss-20b with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use omareng/on-device-LLM-gpt-oss-20b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for omareng/on-device-LLM-gpt-oss-20b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for omareng/on-device-LLM-gpt-oss-20b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for omareng/on-device-LLM-gpt-oss-20b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="omareng/on-device-LLM-gpt-oss-20b", max_seq_length=2048, )
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
GPT-OSS 20B Fine-tuned for Medical Radiology Diagnosis
LoRA fine-tuned version of unsloth/gpt-oss-20b for medical radiology diagnosis tasks.
Model Details
- Base Model:
unsloth/gpt-oss-20b - Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Training Framework: Unsloth
- Task: Medical diagnosis from radiology reports
- Training Dataset: Eurorad medical cases
Installation
pip install unsloth peft transformers accelerate bitsandbytes
Usage
from unsloth import FastLanguageModel
from peft import PeftModel
# Load base model
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/gpt-oss-20b",
dtype=None,
max_seq_length=4096,
load_in_4bit=True,
full_finetuning=False,
)
# Load LoRA adapter
model = PeftModel.from_pretrained(
model,
"omareng/on-device-LLM-gpt-oss-20b",
is_trainable=False
)
# Enable inference mode
FastLanguageModel.for_inference(model)
Training Details
- Framework: Unsloth
- Dataset: Eurorad medical radiology cases
- Optimization: 4-bit quantization
- Sequence Length: 4096 tokens
- Adapter Size: 2.27 GB
Citation
[Citation information will be added upon publication]
Limitations
- Clinical Validation Required: This model has not been clinically validated and should not be used for actual patient diagnosis
- Not for Clinical Use: Not intended for direct patient care without clinical validation
- Research Purposes Only: Designed for research in medical AI and diagnostic systems
- May reflect biases present in training data
- Performance may vary across medical specialties
- Like all LLMs, may generate plausible but incorrect information
Contact
Issues: Please report to this repository
Disclaimer: This model is for research purposes only and has not been approved for clinical use. Always consult qualified healthcare professionals for medical decisions.
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