Instructions to use meomeo163/medical_chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meomeo163/medical_chatbot with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("arcee-ai/Arcee-VyLinh")
model = PeftModel.from_pretrained(base_model, "meomeo163/medical_chatbot")

Transformers

How to use meomeo163/medical_chatbot with Transformers:

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

pipe = pipeline("text-generation", model="meomeo163/medical_chatbot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("meomeo163/medical_chatbot", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meomeo163/medical_chatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meomeo163/medical_chatbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meomeo163/medical_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meomeo163/medical_chatbot

SGLang

How to use meomeo163/medical_chatbot 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 "meomeo163/medical_chatbot" \
    --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": "meomeo163/medical_chatbot",
		"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 "meomeo163/medical_chatbot" \
        --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": "meomeo163/medical_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use meomeo163/medical_chatbot 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 meomeo163/medical_chatbot 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 meomeo163/medical_chatbot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meomeo163/medical_chatbot to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="meomeo163/medical_chatbot",
    max_seq_length=2048,
)

Docker Model Runner
How to use meomeo163/medical_chatbot with Docker Model Runner:
```
docker model run hf.co/meomeo163/medical_chatbot
```

Model Details

Model Description

Model type: Text generation
Language(s) (NLP): Vietnamese
Finetuned from model: arcee-ai/Arcee-VyLinh

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import TextStreamer
from peft import PeftModel
import torch
# Load the model and tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
base_model = AutoModelForCausalLM.from_pretrained("arcee-ai/Arcee-VyLinh")
tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Arcee-VyLinh")
adapter_model = PeftModel.from_pretrained(base_model, "meomeo163/medical_chatbot").to(device)
prompt = "mình đang có hiện tượng bị đau bụng dưới, thì thoảng thấy buồn nôn, các chứng bệnh mình có thể gặp phải là gì"
messages = [
    {"role": "system", "content": "Bạn là trợ lý y tế chuyên nghiệp"},
    {"role_type": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

streamer = TextStreamer(
    tokenizer,
    skip_prompt=True,     
    skip_special_tokens=True 
)

generated_ids = adapter_model.generate(
    model_inputs.input_ids,
    attention_mask=model_inputs.attention_mask, # Add attention mask
    pad_token_id=tokenizer.eos_token_id, # Set pad token id
    max_new_tokens=512,
    eos_token_id=tokenizer.eos_token_id,
    temperature=0.25,
    streamer=streamer,
    no_repeat_ngram_size=3 # Add or increase no_repeat_ngram_size
)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids)[0]
print(response)

Training Details

Training Data

A question-asnwer data about medical [hungnm/vietnamese-medical-qa]

Training Hyperparameters

Training regime:

training_args = TrainingArguments(
    per_device_train_batch_size = 4,
    gradient_accumulation_steps = 4,
    warmup_steps = 100,

    num_train_epochs = 3,
    learning_rate = 2e-4,
    bf16 = True,

    logging_steps = 25,
    output_dir = "finetuned_medical_qa_full",

    optim = "adamw_8bit",
    eval_strategy = "steps",
    eval_steps = 100,
    save_strategy = "steps",
    save_steps = 100,
    
    load_best_model_at_end = True,
    metric_for_best_model = "eval_loss",
    greater_is_better = False,
    save_total_limit = 2,
)

PEFT 0.17.1

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