Instructions to use charakaweb/phi4-clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use charakaweb/phi4-clinical with Transformers:

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

pipe = pipeline("text-generation", model="charakaweb/phi4-clinical", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("charakaweb/phi4-clinical", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("charakaweb/phi4-clinical", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use charakaweb/phi4-clinical with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "charakaweb/phi4-clinical"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "charakaweb/phi4-clinical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/charakaweb/phi4-clinical

SGLang

How to use charakaweb/phi4-clinical 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 "charakaweb/phi4-clinical" \
    --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": "charakaweb/phi4-clinical",
		"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 "charakaweb/phi4-clinical" \
        --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": "charakaweb/phi4-clinical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use charakaweb/phi4-clinical with Docker Model Runner:
```
docker model run hf.co/charakaweb/phi4-clinical
```

Phi-4-mini Clinical (PyTorch / Transformers)

A specialized 3.8B biomedical & clinical reasoning foundation model built on Microsoft's Phi-4-mini-instruct, formatted for standard Hugging Face transformers and PyTorch.

🏗️ 3-Stage Transfer Learning Curriculum

Stage 1 (STEM Foundation): 116,000 instruction pairs across NCERT Classes 6–12 (Physics, Chemistry, Biology) eliminating foundational science hallucinations.
Stage 2 (PubMed 2026 Evidence): 12 recent 2026 clinical update archives from NCBI FTP covering survival outcomes (OS, PFS, HR), targeted therapeutics, and clinical trial endpoints.
Stage 3 (Comprehensive Internal Medicine): Balanced multi-specialty clinical curriculum (cardiology, nephrology, endocrinology, pulmonology) with an active oncology replay buffer.

All LoRA adapter weights have been permanently fused into the base weights.

📊 Benchmark Results (PubMedQA)

Evaluated on 50 biomedical research decision tasks from PubMedQA:

Model	Accuracy	Score	Avg Latency	Relative Improvement
Base Phi-4-mini (4-bit)	26.0%	13 / 50	1.02s / question	Baseline
Phi-4-mini Clinical (Merged)	40.0%	20 / 50	0.93s / question	+53.8% relative gain

⚡ Quickstart with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "charakaweb/phi4-clinical"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "<|user|>\nWhat are the first-line therapeutic recommendations for heart failure with preserved ejection fraction (HFpEF)?<|end|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚕️ Clinical Disclaimer

This model is intended solely for biomedical research, educational exploration, and experimental evaluation. It is not an FDA-cleared medical device and must not be used as a substitute for professional clinical judgment, diagnosis, or treatment.

🏆 Verified Medical Benchmark Results

Benchmark	Scope	Tested Samples	Accuracy	Evaluation Hardware
PubMedQA	Clinical Trial Evidence Decisions	100	49.0%	Apple Silicon Metal GPU
MedQA (USMLE)	Medical Board Diagnostic Cases	100	53.0%	Apple Silicon Metal GPU

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for charakaweb/phi4-clinical

Base model

microsoft/Phi-4-mini-instruct

Finetuned

(131)

this model