Instructions to use towardsinnovationlab/qwen3-medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use towardsinnovationlab/qwen3-medical with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "towardsinnovationlab/qwen3-medical")

Transformers

How to use towardsinnovationlab/qwen3-medical with Transformers:

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

pipe = pipeline("text-generation", model="towardsinnovationlab/qwen3-medical")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("towardsinnovationlab/qwen3-medical", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use towardsinnovationlab/qwen3-medical with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "towardsinnovationlab/qwen3-medical"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "towardsinnovationlab/qwen3-medical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/towardsinnovationlab/qwen3-medical

SGLang

How to use towardsinnovationlab/qwen3-medical 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 "towardsinnovationlab/qwen3-medical" \
    --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": "towardsinnovationlab/qwen3-medical",
		"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 "towardsinnovationlab/qwen3-medical" \
        --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": "towardsinnovationlab/qwen3-medical",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use towardsinnovationlab/qwen3-medical 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 towardsinnovationlab/qwen3-medical 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 towardsinnovationlab/qwen3-medical to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for towardsinnovationlab/qwen3-medical to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="towardsinnovationlab/qwen3-medical",
    max_seq_length=2048,
)

Docker Model Runner
How to use towardsinnovationlab/qwen3-medical with Docker Model Runner:
```
docker model run hf.co/towardsinnovationlab/qwen3-medical
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Adapter Description

This is a LoRA adapter for Qwen/Qwen3-0.6B, trained using the Unsloth library for parameter-efficient fine-tuning (PEFT). When loaded via the transformers pipeline or PeftModel, the base model Qwen/Qwen3-0.6B is automatically fetched, and the adapter is applied on top.

The adapter was trained on the following dataset:

-OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B - for enhancing medical reasoning skills

Model Adapter Details

Developed by: Claudio Giorgio Giancaterino
Language(s) (NLP): English
License: Apache 2.0

Uses

Direct Use

This adapter can be used as support in healthcare applications, medical research, and clinical text generation.

Downstream Use

It can be integrated into educational chatbots for medical reasoning conversations.

Out-of-Scope Use

It is not suitable for high-level decision-making.

Bias, Risks, and Limitations

Conversational quality may degrade with complex or multi-turn inputs. The adapter may give inaccurate answers and should be referred to a professional.

How to Get Started with the Model Adapter

Use the code below to get started with the adapter.

-Using the pipeline:

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

pipe = pipeline("text-generation", model="towardsinnovationlab/qwen3-medical")

messages = [
    {"role": "user", "content": "What are the main symptoms of heart disease? Please provide your answer in bullet points."},
]
result = pipe(messages)
# Extract only the assistant's response
assistant_response = result[0]['generated_text'][-1]['content']
# Remove the <think> tags and their content
clean_response = re.sub(r'<think>.*?</think>', '', assistant_response, flags=re.DOTALL).strip()
print(clean_response)

-Loading the model:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
import re

tokenizer = AutoTokenizer.from_pretrained("towardsinnovationlab/qwen3-medical")
model = AutoModelForCausalLM.from_pretrained("towardsinnovationlab/qwen3-medical")
messages = [
    {"role": "user", "content": "What are the main symptoms of heart disease? Please provide your answer in bullet points."},
]

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=512, 
    temperature=0.7, 
    top_p=0.8, 
    top_k=20,
    do_sample=True  
)

# Extract assistant's response
assistant_response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[-1]:], 
    skip_special_tokens=True
)

# Remove <think> tags and their content
clean_response = re.sub(r'<think>.*?</think>', '', assistant_response, flags=re.DOTALL).strip()

print(clean_response)

Training Details

Training Data

-OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B with 200,193 synthetic medical conversations.

Training Procedure

-Colab with NVIDIA A100 GPU

-per_device_train_batch_size = 4,

-gradient_accumulation_steps = 8,

-warmup_steps = 5,

-max_steps = 30,

-learning_rate = 2e-5,

-logging_steps = 100,

-save_steps=500,

-optim = "adamw_8bit",

-weight_decay = 0.001,

-lr_scheduler_type = "linear"

Results

Loss Value >> 1.4693

Framework versions

PEFT 0.18.0

Downloads last month: 4

Model tree for towardsinnovationlab/qwen3-medical

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

unsloth/Qwen3-0.6B

Adapter

(27)

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Quantizations

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qwen3-medical