Instructions to use aeonium/Aeonium-v1-Base-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aeonium/Aeonium-v1-Base-4B with Transformers:

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

pipe = pipeline("text-generation", model="aeonium/Aeonium-v1-Base-4B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aeonium/Aeonium-v1-Base-4B")
model = AutoModelForCausalLM.from_pretrained("aeonium/Aeonium-v1-Base-4B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aeonium/Aeonium-v1-Base-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aeonium/Aeonium-v1-Base-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aeonium/Aeonium-v1-Base-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aeonium/Aeonium-v1-Base-4B

SGLang

How to use aeonium/Aeonium-v1-Base-4B 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 "aeonium/Aeonium-v1-Base-4B" \
    --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": "aeonium/Aeonium-v1-Base-4B",
		"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 "aeonium/Aeonium-v1-Base-4B" \
        --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": "aeonium/Aeonium-v1-Base-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aeonium/Aeonium-v1-Base-4B with Docker Model Runner:
```
docker model run hf.co/aeonium/Aeonium-v1-Base-4B
```

0x7o commited on Jul 8, 2024

Commit

72b8f29

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@@ -13,15 +13,6 @@ inference:
 # Aeoinum v1 BaseWeb 4B
 A state-of-the-art language model for Russian language processing. This checkpoint contains a preliminary version of the model with 4 billion parameters. Trained only on web pages.
-## Models
-|          Name         | N of parameters | Context window |
-|:---------------------:|:-----------------:|:--------------:|
-| **Aeonium-v1-Base-4B** | 4.04B            | 4K             |
-| Aeonium-v1-Chat-4B     | 4.04B             | 4K             |
-| [Aeonium-v1-Base-1B](https://huggingface.co/aeonium/Aeonium-v1-Base-1B)     | 1.6B             | 4K             |
-| Aeonium-v1-Chat-1B     | 1.6B             | 4K             |
 ## Usage
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM

 # Aeoinum v1 BaseWeb 4B
 A state-of-the-art language model for Russian language processing. This checkpoint contains a preliminary version of the model with 4 billion parameters. Trained only on web pages.
 ## Usage
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM