Instructions to use ars1m/medical_chatbot_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ars1m/medical_chatbot_model with Transformers:

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

pipe = pipeline("text-generation", model="ars1m/medical_chatbot_model")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ars1m/medical_chatbot_model")
model = AutoModelForCausalLM.from_pretrained("ars1m/medical_chatbot_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ars1m/medical_chatbot_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ars1m/medical_chatbot_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ars1m/medical_chatbot_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ars1m/medical_chatbot_model

SGLang

How to use ars1m/medical_chatbot_model 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 "ars1m/medical_chatbot_model" \
    --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": "ars1m/medical_chatbot_model",
		"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 "ars1m/medical_chatbot_model" \
        --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": "ars1m/medical_chatbot_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ars1m/medical_chatbot_model with Docker Model Runner:
```
docker model run hf.co/ars1m/medical_chatbot_model
```

medical_chatbot_model

This model is a fine-tuned version of microsoft/biogpt on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0033

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.1439	0.1686	500	1.9988
1.919	0.3372	1000	1.7011
1.6665	0.5057	1500	1.5434
1.4834	0.6743	2000	1.4272
1.4149	0.8429	2500	1.3405
1.3279	1.0115	3000	1.2766
1.1643	1.1800	3500	1.2236
1.151	1.3486	4000	1.1806
1.1403	1.5172	4500	1.1421
1.0784	1.6858	5000	1.1136
1.0303	1.8543	5500	1.0795
0.8385	2.0229	6000	1.0726
0.8542	2.1915	6500	1.0580
0.7797	2.3601	7000	1.0466
0.8073	2.5287	7500	1.0307
0.7923	2.6972	8000	1.0134
0.7565	2.8658	8500	1.0069

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1

Downloads last month: 12

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for ars1m/medical_chatbot_model

Base model

microsoft/biogpt

Finetuned

(87)

this model