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
```

PhelixZhen commited on Apr 8, 2024

Commit

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@@ -1,3 +1,15 @@
 ---
 license: mit
 ---

 ---
 license: mit
+language:
+- en
 ---
+### 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.