Instructions to use Abeersherif/Medical_Homework2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abeersherif/Medical_Homework2 with Transformers:

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

pipe = pipeline("text-generation", model="Abeersherif/Medical_Homework2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abeersherif/Medical_Homework2")
model = AutoModelForCausalLM.from_pretrained("Abeersherif/Medical_Homework2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Abeersherif/Medical_Homework2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abeersherif/Medical_Homework2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abeersherif/Medical_Homework2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Abeersherif/Medical_Homework2

SGLang

How to use Abeersherif/Medical_Homework2 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 "Abeersherif/Medical_Homework2" \
    --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": "Abeersherif/Medical_Homework2",
		"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 "Abeersherif/Medical_Homework2" \
        --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": "Abeersherif/Medical_Homework2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Abeersherif/Medical_Homework2 with Docker Model Runner:
```
docker model run hf.co/Abeersherif/Medical_Homework2
```

Medical_Homework2 — Fine-Tuned SmolLM2-1.7B for Medical Reasoning

Medical_Homework2 is a fine-tuned version of SmolAI/SmolLM2-1.7B, trained specifically on structured medical question-answer data and short reasoning tasks.
The model aims to provide concise, accurate, and educational medical explanations suitable for students and basic learning purposes.

Model Overview

This model is optimized for medical comprehension tasks such as:

Short medical answers
Step-by-step reasoning
Explanations of conditions, symptoms, and basic physiology
Educational or homework-style responses

It is not designed for professional medical diagnosis or treatment decisions.

Intended Use

Recommended Use Cases

Medical homework and assignment assistance
Explanation of medical concepts in simple language
Introductory physiology and pathology topics
Basic reasoning about medical questions

Not Recommended

Real-world clinical decision-making
Emergency or diagnostic use
Any situation requiring professional medical judgement

Training Data

The model was fine-tuned using:

Synthetic medical question-answer pairs
Simplified educational medical explanations
Instruction-answer examples
Homework-style reasoning data

No real patient data or clinical records were used.

Training Details

Base model: SmolAI/SmolLM2-1.7B
Fine-tuning objective: Causal language modeling
Method: Full or LoRA fine-tuning (depending on your actual setup)
Optimizer: AdamW
Typical epochs: 1–3

If you want, a full training script section can be added.

Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Abeersherif/Medical_Homework2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain what type 2 diabetes is in simple terms."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 9

Safetensors

Model size

0.1B params

Tensor type

F32

Abeersherif
/

Medical_Homework2