Instructions to use Nexusflow/Athene-V2-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexusflow/Athene-V2-Chat with Transformers:

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

pipe = pipeline("text-generation", model="Nexusflow/Athene-V2-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nexusflow/Athene-V2-Chat")
model = AutoModelForCausalLM.from_pretrained("Nexusflow/Athene-V2-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nexusflow/Athene-V2-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexusflow/Athene-V2-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexusflow/Athene-V2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nexusflow/Athene-V2-Chat

SGLang

How to use Nexusflow/Athene-V2-Chat 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 "Nexusflow/Athene-V2-Chat" \
    --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": "Nexusflow/Athene-V2-Chat",
		"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 "Nexusflow/Athene-V2-Chat" \
        --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": "Nexusflow/Athene-V2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexusflow/Athene-V2-Chat with Docker Model Runner:
```
docker model run hf.co/Nexusflow/Athene-V2-Chat
```

BEST SYSTEM PROMPT. HUGE PERFORMANCE IMPROVEMENT

by rombodawg - opened Feb 20, 2025

Discussion

rombodawg

Feb 20, 2025

•

edited Feb 20, 2025

Here is what i use, i saw huge improvemnets

System prompt:

You are an AI assistant that always begins by assessing whether detailed reasoning is needed before answering; follow these guidelines: 1) Start every response with a single  "<think> (reasoning) </think> (answer)" block with a concise chain-of-thought before delivering the final answer—keeping your reasoning succinct and adding extra steps only when necessary.

AlioLeuchtmann

Mar 15, 2025

Thank you for sharing your experience, where exactly did you see the improvement and how did you measure it, vibes or benchmarking ?

rombodawg

Mar 15, 2025

@HondaVfr800 I always check for real world performance. I guess you can call it "Vibe checking". But I have found testing in person and actually having a human evaluate results gives a better indication of model performance than a benchmark at least when doing many various tests.

rombodawg changed discussion status to closed Mar 15, 2025

Smorty100

Mar 18, 2025

is there a specific reason you put a dash between "answer" and "keeping" or was that a typo?

I often see weird "typos" like this in system prompts, which is why I am asking..

rombodawg

Mar 19, 2025

@Smorty100 I had it spell checked my chatgpt thats prolly why

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