Instructions to use PhelixZhen/Algae-550M-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PhelixZhen/Algae-550M-base with Transformers:

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

pipe = pipeline("text-generation", model="PhelixZhen/Algae-550M-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PhelixZhen/Algae-550M-base")
model = AutoModelForCausalLM.from_pretrained("PhelixZhen/Algae-550M-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PhelixZhen/Algae-550M-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PhelixZhen/Algae-550M-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PhelixZhen/Algae-550M-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PhelixZhen/Algae-550M-base

SGLang

How to use PhelixZhen/Algae-550M-base 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 "PhelixZhen/Algae-550M-base" \
    --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": "PhelixZhen/Algae-550M-base",
		"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 "PhelixZhen/Algae-550M-base" \
        --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": "PhelixZhen/Algae-550M-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PhelixZhen/Algae-550M-base with Docker Model Runner:
```
docker model run hf.co/PhelixZhen/Algae-550M-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is the base model of Algae-550M.

This model was trained on a 35GB dataset using bf16 precision and completed 1.8 epochs. It performs well in answering questions, achieving a score of up to 45.2 in TruthfulQA (mc2), surpassing GPT-2 (40.6). Other metrics align with models of equivalent training and parameter volume.

This model was trained using open-source datasets. All work was completed solely by the author. Given that the author is currently a high school student without formal systematic training, any questions or suggestions are welcome.

It's important to note that the version of the model released here is not necessarily the one with the best performance in testing, but rather a version with improved overall language comprehension abilities.

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Safetensors

Model size

0.6B params

Tensor type

BF16

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Quantizations

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