Instructions to use ibivibiv/athena-120b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibivibiv/athena-120b with Transformers:

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

pipe = pipeline("text-generation", model="ibivibiv/athena-120b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibivibiv/athena-120b")
model = AutoModelForCausalLM.from_pretrained("ibivibiv/athena-120b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibivibiv/athena-120b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibivibiv/athena-120b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibivibiv/athena-120b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibivibiv/athena-120b

SGLang

How to use ibivibiv/athena-120b 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 "ibivibiv/athena-120b" \
    --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": "ibivibiv/athena-120b",
		"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 "ibivibiv/athena-120b" \
        --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": "ibivibiv/athena-120b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ibivibiv/athena-120b with Docker Model Runner:
```
docker model run hf.co/ibivibiv/athena-120b
```

athena-120b / model-00023-of-00106.safetensors

ibivibiv

Upload LlamaForCausalLM

45ed2e0 over 2 years ago

Download

History Blame Contribute Delete

4.36 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 96d0768e224a2b7da7314d54a610cfb61e2ed89ac4f1f1942bc3553cec0c00b8
Size of remote file:: 4.36 GB
SHA256:: 2d322bb8653aa4929901b9119dfa0fdcd6633b05d0741b48213c6612e57b76b7

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.