Instructions to use ASTRAI-labs/pluto-nano-0.5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ASTRAI-labs/pluto-nano-0.5-base with Transformers:

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

pipe = pipeline("text-generation", model="ASTRAI-labs/pluto-nano-0.5-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ASTRAI-labs/pluto-nano-0.5-base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ASTRAI-labs/pluto-nano-0.5-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ASTRAI-labs/pluto-nano-0.5-base

SGLang

How to use ASTRAI-labs/pluto-nano-0.5-base 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 "ASTRAI-labs/pluto-nano-0.5-base" \
    --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": "ASTRAI-labs/pluto-nano-0.5-base",
		"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 "ASTRAI-labs/pluto-nano-0.5-base" \
        --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": "ASTRAI-labs/pluto-nano-0.5-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ASTRAI-labs/pluto-nano-0.5-base with Docker Model Runner:
```
docker model run hf.co/ASTRAI-labs/pluto-nano-0.5-base
```

ASTRAI Pluto Nano 0.5 — BASE

Pre-identity / pre-final-preference checkpoint of Pluto Nano 0.5.

This is the v11 checkpoint before identity SFT, ORPO, and KTO-math. Use this as the starting point if you want to fine-tune your own identity, style or preference on top of Pluto Nano.

For the production-aligned model, use pluto-nano-0.5.

Architecture

1 B total / ~50 M active per token (35 experts, top-1 MoE)
GQA 6 query / 2 KV heads
16 layers, hidden 384, expert intermediate 1536
Tokenizer: custom 32 k BPE
Languages: EN, PT, ES, ZH, HI
Context: 4096

Training

Pretrain: 13 B tokens multilingual
Distill v1/v2 (frontier models)
Recovery CPT + Wikipedia knowledge boost
Second Distill (e1 best): reasoning + chat + QA + replay buffer, 30 M tokens
Trained entirely on RTX 3060 12 GB
Total wall-clock: ~2 weeks

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("ASTRAI-labs/pluto-nano-0.5-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "ASTRAI-labs/pluto-nano-0.5-base",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).cuda()

License

ASTRAI Closed License. See pluto-nano-0.5 for full terms.

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for ASTRAI-labs/pluto-nano-0.5-base

Finetunes

1 model