Instructions to use ayuag/yukt-med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayuag/yukt-med with Transformers:

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

pipe = pipeline("text-generation", model="ayuag/yukt-med")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayuag/yukt-med")
model = AutoModelForCausalLM.from_pretrained("ayuag/yukt-med")

llama-cpp-python

How to use ayuag/yukt-med with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ayuag/yukt-med",
	filename="yukt-med-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ayuag/yukt-med with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ayuag/yukt-med:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ayuag/yukt-med:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ayuag/yukt-med:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ayuag/yukt-med:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ayuag/yukt-med:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ayuag/yukt-med:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ayuag/yukt-med:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ayuag/yukt-med:Q4_K_M

Use Docker

docker model run hf.co/ayuag/yukt-med:Q4_K_M

LM Studio
Jan

vLLM

How to use ayuag/yukt-med with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayuag/yukt-med"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayuag/yukt-med",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ayuag/yukt-med:Q4_K_M

SGLang

How to use ayuag/yukt-med 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 "ayuag/yukt-med" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayuag/yukt-med",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ayuag/yukt-med" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayuag/yukt-med",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use ayuag/yukt-med with Ollama:
```
ollama run hf.co/ayuag/yukt-med:Q4_K_M
```

Unsloth Studio

How to use ayuag/yukt-med 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 ayuag/yukt-med 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 ayuag/yukt-med to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ayuag/yukt-med to start chatting

Docker Model Runner
How to use ayuag/yukt-med with Docker Model Runner:
```
docker model run hf.co/ayuag/yukt-med:Q4_K_M
```

Lemonade

How to use ayuag/yukt-med with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ayuag/yukt-med:Q4_K_M

Run and chat with the model

lemonade run user.yukt-med-Q4_K_M

List all available models

lemonade list

🩺 Yukt-Med (Phi-2 Medical Assistant)

Your Compact, Specialized Medical Knowledge Companion.

Fine-tuned on 86,000+ curated medical interactions to provide concise, accurate, and non-diagnostic healthcare information.

🌟 Overview

Yukt-Med is a lightweight, state-of-the-art language model designed for the medical and healthcare domain. It is fine-tuned using LoRA (Low-Rank Adaptation) on a diverse collection of healthcare datasets.

What makes Yukt-Med unique is its balance of performance and efficiency. While powerful, it has been quantized to 4-bit GGUF, making it runnable on commodity hardware, mobile devices, and in offline environments.

💡 Perfect for: Rapid medical information retrieval, symptom analysis support, and educational purposes. Not for diagnosis.

🚀 Key Features

Feature	Description
🧠 Specialized Brain	Trained on ChatDoctor, MedQuad, and curated drug databases.
⚡ Ultra-Efficient	GGUF version runs smoothly on 4GB RAM (CPU/Mobile).
Instruction-Following	Responds accurately to instructions using a specific prompt template.
🌐 Production-Ready	Available in Standard Safetensors and Compact GGUF formats.

📊 Training Data

The model's knowledge comes from over 86,800 cleaned and structured examples:

ChatDoctor Dataset: Real-world patient-doctor dialogues for conversational medical advice.
MedQuad Dataset: Large-scale Medical Question-Answering pairs.
Drugs & Side Effects: Detailed information on pharmaceuticals.
Symptom-Disease Mapping: Patterns for common medical conditions.

(Note: Data was filtered to ensure high-quality, safe, and factual content.)

🧪 Evaluation Examples

Compare Yukt-Med's focused medical responses:

Instruction (Prompt)	Yukt-Med Response (Generation)
### Instruction: What are the common symptoms of a common cold? ### Response:	The common symptoms of a common cold include a runny or stuffy nose, sneezing, sore throat, cough, and mild body aches or fatigue.
### Instruction: Tell me about the side effects of Aspirin. ### Response:	The most frequent side effects are bleeding and stomach upset.

🛠️ Usage Instructions

1. Using Standard Transformers (Python/GPU)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ayuag/yukt-med"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

def ask_medical_bot(question):
    prompt = f"### Instruction: {question}\n### Response:"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(ask_medical_bot("What are symptoms of Malaria?"))

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

F16

Model tree for ayuag/yukt-med

Base model

microsoft/phi-2

Adapter

(989)

this model

ayuag
/

yukt-med