Instructions to use FreedomIntelligence/Apollo-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomIntelligence/Apollo-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="FreedomIntelligence/Apollo-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/Apollo-0.5B")
model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/Apollo-0.5B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FreedomIntelligence/Apollo-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomIntelligence/Apollo-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreedomIntelligence/Apollo-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomIntelligence/Apollo-0.5B

SGLang

How to use FreedomIntelligence/Apollo-0.5B 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-0.5B" \
    --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": "FreedomIntelligence/Apollo-0.5B",
		"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 "FreedomIntelligence/Apollo-0.5B" \
        --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": "FreedomIntelligence/Apollo-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FreedomIntelligence/Apollo-0.5B with Docker Model Runner:
```
docker model run hf.co/FreedomIntelligence/Apollo-0.5B
```

Xidong commited on Mar 7, 2024

Commit

91303f9

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@@ -54,6 +54,14 @@ Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
 Please use the following citation if you intend to use our dataset for training or evaluation:
 ```
 @misc{Apollo,
   title={Apollo, Multilingual Medicine: Model, Dataset, Benchmark, Code},
   author={Xidong Wang, Junyin Chen, Nuo Chen, Yidong Wang, Zhiyi Zhang, Benyou Wang},

 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}
+}
 @misc{Apollo,
   title={Apollo, Multilingual Medicine: Model, Dataset, Benchmark, Code},
   author={Xidong Wang, Junyin Chen, Nuo Chen, Yidong Wang, Zhiyi Zhang, Benyou Wang},