Instructions to use Mouhib007/DeepSeek-r1-Medical-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mouhib007/DeepSeek-r1-Medical-Mini with Transformers:

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

pipe = pipeline("text-generation", model="Mouhib007/DeepSeek-r1-Medical-Mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mouhib007/DeepSeek-r1-Medical-Mini")
model = AutoModelForCausalLM.from_pretrained("Mouhib007/DeepSeek-r1-Medical-Mini", 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 Mouhib007/DeepSeek-r1-Medical-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mouhib007/DeepSeek-r1-Medical-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mouhib007/DeepSeek-r1-Medical-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mouhib007/DeepSeek-r1-Medical-Mini

SGLang

How to use Mouhib007/DeepSeek-r1-Medical-Mini 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 "Mouhib007/DeepSeek-r1-Medical-Mini" \
    --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": "Mouhib007/DeepSeek-r1-Medical-Mini",
		"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 "Mouhib007/DeepSeek-r1-Medical-Mini" \
        --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": "Mouhib007/DeepSeek-r1-Medical-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Mouhib007/DeepSeek-r1-Medical-Mini 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 Mouhib007/DeepSeek-r1-Medical-Mini 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 Mouhib007/DeepSeek-r1-Medical-Mini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mouhib007/DeepSeek-r1-Medical-Mini to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Mouhib007/DeepSeek-r1-Medical-Mini",
    max_seq_length=2048,
)

Docker Model Runner
How to use Mouhib007/DeepSeek-r1-Medical-Mini with Docker Model Runner:
```
docker model run hf.co/Mouhib007/DeepSeek-r1-Medical-Mini
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Details

Model Description

This model is a fine-tuned version of the unsloth/DeepSeek-R1-Distill-Llama-8B model, specifically adapted for medical reasoning tasks. The fine-tuning process utilized the FreedomIntelligence/medical-o1-reasoning-SFT dataset, which focuses on complex chain-of-thought (CoT) reasoning in the medical domain. The model has been optimized using the unsloth and trl libraries, with LoRA (Low-Rank Adaptation) techniques applied to enhance performance while maintaining efficiency.

Developed by: [Mohamed Mouhib Naffeti]
Finetuned from model: [unsloth/DeepSeek-R1-Distill-Llama-8B]

Model Sources

Demo : https://www.kaggle.com/code/mohamednaffeti007/fine-tune-deepseek-model

Uses

This model is intended for use in medical reasoning tasks, particularly those requiring complex chain-of-thought reasoning. It can be used to generate responses to medical questions, provide explanations, and assist in medical decision-making processes.

Downstream Use

The model can be further fine-tuned for specific medical subdomains or integrated into larger healthcare applications, such as diagnostic tools, medical chatbots, or educational platforms.

Out-of-Scope Use

This model is not intended for use in high-stakes medical decision-making without human oversight. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.

Bias, Risks, and Limitations

The model may inherit biases present in the training data, which could affect its performance on certain medical topics or populations. Additionally, the model's responses should be carefully validated, as it may generate incorrect or misleading information.

Recommendations

Users should be aware of the model's limitations and validate its outputs, especially in critical medical scenarios. It is recommended to use the model in conjunction with human expertise and to continuously monitor its performance.

Training Hyperparameters

Training regime: Mixed precision (fp16/bf16)

Batch size: 2 per device

Gradient accumulation steps: 4

Epochs: 1

Learning rate: 2e-4

Optimizer: AdamW 8-bit

Weight decay: 0.01

Warmup steps: 5

Max steps: 60

LoRA configuration:

Rank (r): 16

Alpha: 16

Dropout: 0

Target modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

Metrics

you'll find the metrics result here : https://wandb.ai/contact-mohamednaffeti-isimm/Fine-Tune-DeepSeek-Model-R1%20On%20Medical%20Dataset/runs/evop6kph?nw=nwusercontactmohamednaffeti

Model Card Contact

contact.mohamednaffeti@gmail.com

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Model tree for Mouhib007/DeepSeek-r1-Medical-Mini

Base model

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Finetuned

unsloth/DeepSeek-R1-Distill-Llama-8B