Instructions to use syubraj/Phi3.5-medicalchat-unsloth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use syubraj/Phi3.5-medicalchat-unsloth with Transformers:

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

pipe = pipeline("text-generation", model="syubraj/Phi3.5-medicalchat-unsloth")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("syubraj/Phi3.5-medicalchat-unsloth")
model = AutoModelForCausalLM.from_pretrained("syubraj/Phi3.5-medicalchat-unsloth", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use syubraj/Phi3.5-medicalchat-unsloth with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "syubraj/Phi3.5-medicalchat-unsloth"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "syubraj/Phi3.5-medicalchat-unsloth",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/syubraj/Phi3.5-medicalchat-unsloth

SGLang

How to use syubraj/Phi3.5-medicalchat-unsloth 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 "syubraj/Phi3.5-medicalchat-unsloth" \
    --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": "syubraj/Phi3.5-medicalchat-unsloth",
		"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 "syubraj/Phi3.5-medicalchat-unsloth" \
        --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": "syubraj/Phi3.5-medicalchat-unsloth",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use syubraj/Phi3.5-medicalchat-unsloth with Docker Model Runner:
```
docker model run hf.co/syubraj/Phi3.5-medicalchat-unsloth
```

Model Card for Model ID

Model Details

🏥 Phi-3.5 Mini Instruct Medical Chat (Unsloth)

The MedicalChat-Phi-3.5-mini-instruct- fine-tuned model is designed to simulate doctor-patient conversations, offering medical consultations and suggestions based on patient queries. However, its accuracy may be limited in real-world scenarios, as the training dataset was relatively small.

🔍 Model Overview

🧑‍⚕️ Developed by: syubraj
📜 Model Type: Causal Language Model (CausalLM)
🗣️ Language: English
📜 License: MIT License
🛠️ Fine-tuned From: microsoft/phi-3.5-mini-instruct

Uses

Direct Use

!pip install unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained("syubraj/Phi3.5-medicalchat-unsloth",
                                                      max_seq_length = 1024,
                                                      load_in_4bit = True,
                                                      dtype = None
)
user_query = "<Your medical query here>"
system_prompt = """You are a trusted AI-powered medical assistant. Analyze patient queries carefully and provide accurate, professional, and empathetic responses. Prioritize patient safety, adhere to medical best practices, and recommend consulting a healthcare provider when necessary."""


message = [
    {"role": "system", "content": system_prompt},
    {"role": "human", "content": user_query}
]

# Creating message based on tokenizers chat template
prompt = tokenizer.apply_chat_template(message, tokenize = False, add_generation_prompt = True)

FastLanguageModel.for_inference(model)

# Tokenizing inputs
inputs = tokenizer(prompt, return_tensors = "pt").to("cuda")

# Output Generated
outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True) # Change the `max_new_tokens` according to required objective
tokenizer.batch_decode(outputs)

Model Results

Step	Training Loss
10	2.53
20	2.20
30	1.95
40	2.01
50	1.97
60	2.02

Downloads last month: 11

Safetensors

Model size

4B params

Tensor type

F16

Model tree for syubraj/Phi3.5-medicalchat-unsloth

Base model

microsoft/Phi-3.5-mini-instruct

Finetuned

(336)

this model

Dataset used to train syubraj/Phi3.5-medicalchat-unsloth

Collection including syubraj/Phi3.5-medicalchat-unsloth

Phi3.5-MedicalChat

Collection

4 items • Updated Mar 28, 2025 • 1