Instructions to use FreedomIntelligence/Apollo-6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/Apollo-6B with Transformers:

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

pipe = pipeline("text-generation", model="FreedomIntelligence/Apollo-6B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/Apollo-6B")
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/Apollo-6B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FreedomIntelligence/Apollo-6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/Apollo-6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/Apollo-6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/Apollo-6B

SGLang

How to use FreedomIntelligence/Apollo-6B 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 "FreedomIntelligence/Apollo-6B" \
    --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": "FreedomIntelligence/Apollo-6B",
		"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 "FreedomIntelligence/Apollo-6B" \
        --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": "FreedomIntelligence/Apollo-6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FreedomIntelligence/Apollo-6B with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/Apollo-6B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Multilingual Medicine: Model, Dataset, Benchmark, Code

Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far

👨🏻‍💻Github •📃 Paper • 🌐 Demo • 🤗 ApolloCorpus • 🤗 XMedBench
中文 | English

🌈 Update

[2024.03.07] Paper released.
[2024.02.12] ApolloCorpus and XMedBench is published！🎉
[2024.01.23] Apollo repo is published！🎉

Results

🤗Apollo-0.5B • 🤗 Apollo-1.8B • 🤗 Apollo-2B • 🤗 Apollo-6B • 🤗 Apollo-7B

🤗 Apollo-0.5B-GGUF • 🤗 Apollo-2B-GGUF • 🤗 Apollo-6B-GGUF • 🤗 Apollo-7B-GGUF

Usage Format

User:{query}\nAssistant:{response}<|endoftext|>

Dataset & Evaluation

Dataset 🤗 ApolloCorpus
Click to expand
- Zip File
- Data category
  - Pretrain:
    - data item:
      
      json_name: {data_source}{language}{data_type}.json
      
      data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
      
      language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
      
      data_type: qa(generated qa from text)
      
      data_type==text: list of string
      [ "string1", "string2", ... ]
      
      data_type==qa: list of qa pairs(list of string)
      [ [ "q1", "a1", "q2", "a2", ... ], ... ]
  - SFT:
    - json_name: {data_source}_{language}.json
    - data_type: code, general, math, medicalExam, medicalPatient
    - data item: list of qa pairs(list of string)
      [ [ "q1", "a1", "q2", "a2", ... ], ... ]
Evaluation 🤗 XMedBench
Click to expand
- EN:
  - MedQA-USMLE
  - MedMCQA
  - PubMedQA: Because the results fluctuated too much, they were not used in the paper.
  - MMLU-Medical
    - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
  - MedQA-MCMLE
  - CMB-single: Not used in the paper
    - Randomly sample 2,000 multiple-choice questions with single answer.
  - CMMLU-Medical
    - Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
  - CExam: Not used in the paper
    - Randomly sample 2,000 multiple-choice questions
- ES: Head_qa
- FR: Frenchmedmcqa
- HI: MMLU_HI
  - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: MMLU_Ara
  - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine

Results reproduction

Click to expand

Waiting for Update

Citation

Please use the following citation if you intend to use our dataset for training or evaluation:

@misc{wang2024apollo,
   title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
   author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
   year={2024},
   eprint={2403.03640},
   archivePrefix={arXiv},
   primaryClass={cs.CL}
}