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", device_map="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 Desktop
Docker Model Runner
How to use towardsinnovationlab/qwen3-medical with Docker Model Runner:
```
docker model run hf.co/towardsinnovationlab/qwen3-medical
```

towardsinnovationlab commited on Feb 28

Commit

c54a353

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Update README.md

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@@ -16,15 +16,16 @@ language:
 - en
 ---
-### Model Description
-This model is a fine-tuned version of Qwen/Qwen3-0.6B using the Unsloth library and LoRA for parameter-efficient training.
-The model is trained on the following dataset:
 -OpenMed/Medical-Reasoning-SFT-GPT-OSS-120B - for enhancing medical reasoning skills
-### Model Details
 - **Developed by:** Claudio Giorgio Giancaterino
 - **Language(s) (NLP):** English
@@ -35,7 +36,7 @@ The model is trained on the following dataset:
 ### Direct Use
-This model can be used as support in healthcare applications, medical research, and clinical text generation.
 ### Downstream Use
@@ -48,11 +49,11 @@ It is not suitable for high-level decision-making.
 ## Bias, Risks, and Limitations
 Conversational quality may degrade with complex or multi-turn inputs.
-The model may give inaccurate answers and should be referred to a professional.
-## How to Get Started with the Model
-Use the code below to get started with the model.
 -Using the pipeline:
 ```python

 - en
 ---
+### 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
 ### Direct Use
+This adapter can be used as support in healthcare applications, medical research, and clinical text generation.
 ### Downstream Use
 ## 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:
 ```python