Instructions to use Aryanne/Astrea-RP-v1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aryanne/Astrea-RP-v1-3B with Transformers:

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

pipe = pipeline("text-generation", model="Aryanne/Astrea-RP-v1-3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Aryanne/Astrea-RP-v1-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Aryanne/Astrea-RP-v1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aryanne/Astrea-RP-v1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aryanne/Astrea-RP-v1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aryanne/Astrea-RP-v1-3B

SGLang

How to use Aryanne/Astrea-RP-v1-3B 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 "Aryanne/Astrea-RP-v1-3B" \
    --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": "Aryanne/Astrea-RP-v1-3B",
		"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 "Aryanne/Astrea-RP-v1-3B" \
        --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": "Aryanne/Astrea-RP-v1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aryanne/Astrea-RP-v1-3B with Docker Model Runner:
```
docker model run hf.co/Aryanne/Astrea-RP-v1-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is a merge of euclaise/Echo-3B, stabilityai/stablelm-zephyr-3b and Aryanne/Astridboros-3B using task_arithmetic(see astrea-rp-v1-3b.yml or below).

merge_method: task_arithmetic
base_model: euclaise/Ferret-3B
models:
  - model: euclaise/Ferret-3B
  - model: stabilityai/stablelm-zephyr-3b
    parameters:
      weight: 0.33
  - model: euclaise/Echo-3B
    parameters:
      weight: 0.66
  - model: Aryanne/Astridboros-3B
    parameters:
      weight: 0.16
dtype: float16

I recommend the use of Vicuna prompt format, but it's your choice to see what works for you.

GGUF quants: afrideva/Astrea-RP-v1-3B-GGUF

I think zephyr license applies to this merge, for non commercial use.

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for Aryanne/Astrea-RP-v1-3B

Merges

2 models

Quantizations

1 model