Instructions to use abouelgoud/NextecHealth-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abouelgoud/NextecHealth-r1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
model = PeftModel.from_pretrained(base_model, "abouelgoud/NextecHealth-r1")

Transformers

How to use abouelgoud/NextecHealth-r1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="abouelgoud/NextecHealth-r1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("abouelgoud/NextecHealth-r1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abouelgoud/NextecHealth-r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abouelgoud/NextecHealth-r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abouelgoud/NextecHealth-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abouelgoud/NextecHealth-r1

SGLang

How to use abouelgoud/NextecHealth-r1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abouelgoud/NextecHealth-r1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abouelgoud/NextecHealth-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abouelgoud/NextecHealth-r1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abouelgoud/NextecHealth-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abouelgoud/NextecHealth-r1 with Docker Model Runner:
```
docker model run hf.co/abouelgoud/NextecHealth-r1
```

NextecHealth-r1

A healthcare-focused reasoning language model optimized for medical question answering, clinical reasoning, and healthcare knowledge tasks. Model Description NextecHealth-r1 is a domain-specialized language model designed to improve reasoning capabilities in healthcare and medical applications. The model is intended to assist with medical information retrieval, clinical reasoning, educational use, and biomedical question answering while maintaining transparency about its limitations. Model name: NextecHealth-r1 Repository: abouelgoud/NextecHealth-r1 Developer: Abou Elgoud Language: English

Intended Use

Primary Use Cases Medical question answering Clinical reasoning assistance Medical education Biomedical knowledge exploration Healthcare research support Summarization of medical literature Medical chatbot development (with human oversight) Out-of-Scope Use This model must not be used as a replacement for qualified healthcare professionals. It should not be relied upon for: Medical diagnosis Emergency decision making Prescribing medications Treatment planning without clinician review Any safety-critical medical decisions Outputs should always be verified by qualified medical professionals.

Training

NextecHealth-r1 was developed through domain-specific adaptation to improve reasoning over healthcare and medical content. The model emphasizes: Clinical reasoning Medical terminology understanding Biomedical knowledge Multi-step reasoning Instruction following

PEFT 0.19.1

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Model tree for abouelgoud/NextecHealth-r1

Base model

microsoft/Phi-3-mini-4k-instruct

Adapter

(862)

this model