Instructions to use bullerwins/Athene-70B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bullerwins/Athene-70B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bullerwins/Athene-70B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bullerwins/Athene-70B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf bullerwins/Athene-70B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bullerwins/Athene-70B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bullerwins/Athene-70B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bullerwins/Athene-70B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf bullerwins/Athene-70B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bullerwins/Athene-70B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf bullerwins/Athene-70B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bullerwins/Athene-70B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bullerwins/Athene-70B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use bullerwins/Athene-70B-GGUF with Ollama:
ollama run hf.co/bullerwins/Athene-70B-GGUF:Q4_K_M
- Unsloth Studio
How to use bullerwins/Athene-70B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bullerwins/Athene-70B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for bullerwins/Athene-70B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bullerwins/Athene-70B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use bullerwins/Athene-70B-GGUF with Docker Model Runner:
docker model run hf.co/bullerwins/Athene-70B-GGUF:Q4_K_M
- Lemonade
How to use bullerwins/Athene-70B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bullerwins/Athene-70B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Athene-70B-GGUF-Q4_K_M
List all available models
lemonade list
Quantized GGUF version using llama.cpp
Original model Nexusflow/Athene-70B
Athene-Llama3-70B
We introduce Athene-Llama3-70B, an open-weights LLM trained through RLHF based off Llama-3-70B-Instruct. Athene-70B achieves a high score on Arena-Hard-Auto, a proxy benchmark for Chatbot Arena.
- Developed by: The Nexusflow Team (Evan Frick*, Peter Jin*, Tianle Li*, Karthik Ganesan, Jian Zhang, Jiantao Jiao and Banghua Zhu).
- Model type: Chat Model
- Finetuned from model: Llama-3-70B-Instruct.
Blog: https://nexusflow.ai/blogs/athene
| Model | Arena-Hard |
|---|---|
| Claude-3.5-Sonnet (Proprietary) | 79.3% |
| GPT-4o (Proprietary) | 79.2% |
| Athene-70B (Open) | 77.8% |
| Gemini-Pro-1.5 (Proprietary) | 72.0% |
| Gemma-2-27B (Open) | 57.0% |
| Llama-3-70B (Open) | 46.6% |
Usage
Athene-70B uses the same chat template as Llama-3-70B-Instruct. Below is an example simple usage using the Transformers library.
import transformers
import torch
model_id = "Nexusflow/Athene-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an Athene Noctura, you can only speak with owl sounds. Whoooo whooo."},
{"role": "user", "content": "Whooo are you?"},
]
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|end_of_text|>")
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
Acknowledgment
We would like to thank the LMSYS Organization for their support of online demo and private test. We would like to thank Meta AI and the open source community for their efforts in providing the datasets and base models.
Citation
@misc{Athene2024,
title = {Athene-70B: Redefining the Boundaries of Post-Training for Open Models},
url = {https://nexusflow.ai/blogs/athene},
author = {Frick, Evan and Jin, Peter and Li, Tianle and Ganesan, Karthik and Zhang, Jian and Jiao, Jiantao and Zhu, Banghua},
month = {July},
year = {2024}
}
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