Instructions to use Jip7e/DocMed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jip7e/DocMed with Transformers:

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

pipe = pipeline("text-generation", model="Jip7e/DocMed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jip7e/DocMed")
model = AutoModelForCausalLM.from_pretrained("Jip7e/DocMed")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Jip7e/DocMed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jip7e/DocMed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jip7e/DocMed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jip7e/DocMed

SGLang

How to use Jip7e/DocMed 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 "Jip7e/DocMed" \
    --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": "Jip7e/DocMed",
		"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 "Jip7e/DocMed" \
        --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": "Jip7e/DocMed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jip7e/DocMed with Docker Model Runner:
```
docker model run hf.co/Jip7e/DocMed
```

🩺 DocMed

DocMed is a lightweight medical language model designed to assist medical students with learning and revision.

It provides clear, concise explanations of medical concepts in a friendly, study-oriented style.

❗ Disclaimer

DocMed is an educational model only.
It must not be used for diagnosis, treatment, or clinical decision-making.

🔍 Capabilities

Medical concept explanations
Exam-style question answering
Mechanism summaries (physiology, pharmacology, pathology)
Short, student-friendly responses with emojis 🩺

🚫 Limitations

Not a clinical decision system
Not guaranteed to be factually correct
Not a replacement for textbooks or professors

🧠 Model Origin

DocMed was trained using modern parameter-efficient fine-tuning techniques on top of an open-source language model architecture.

📜 License & Attribution

This model is released under the Apache 2.0 license.
It is a derivative work of open-source language model research.

👤 Author

DocMed is an independent educational AI project.

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for Jip7e/DocMed

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(555)

this model

Jip7e
/

DocMed