Instructions to use celestialcreator/axon-smollm2-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use celestialcreator/axon-smollm2-360m with Transformers:

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

pipe = pipeline("text-generation", model="celestialcreator/axon-smollm2-360m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("celestialcreator/axon-smollm2-360m")
model = AutoModelForCausalLM.from_pretrained("celestialcreator/axon-smollm2-360m")
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]:]))

Transformers.js

How to use celestialcreator/axon-smollm2-360m with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'celestialcreator/axon-smollm2-360m');

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use celestialcreator/axon-smollm2-360m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "celestialcreator/axon-smollm2-360m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "celestialcreator/axon-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/celestialcreator/axon-smollm2-360m

SGLang

How to use celestialcreator/axon-smollm2-360m 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 "celestialcreator/axon-smollm2-360m" \
    --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": "celestialcreator/axon-smollm2-360m",
		"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 "celestialcreator/axon-smollm2-360m" \
        --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": "celestialcreator/axon-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use celestialcreator/axon-smollm2-360m with Docker Model Runner:
```
docker model run hf.co/celestialcreator/axon-smollm2-360m
```

Axon — SmolLM2-360M Portfolio Assistant

Fine-tuned SmolLM2-360M-Instruct to serve as a personal portfolio AI assistant. Runs entirely in the browser via WebGPU + Transformers.js.

What is Axon?

Axon is a client-side AI assistant embedded in a Three.js 3D portfolio website. It answers questions about the portfolio owner (Akshay Mhaskar) — work experience, skills, projects, and contact info. Zero backend, zero API costs, fully private.

Training

Method: LoRA (rank=32, alpha=64) fine-tuning via TRL SFTTrainer
Dataset: 1,001 curated chat examples (knowledge Q&A, refusal training, edge cases)
Hardware: NVIDIA RTX 5090
Base model: SmolLM2-360M-Instruct
Precision: bf16
Train loss: 1.33
Eval loss: 0.94
Token accuracy: 80%
Training time: 6 minutes

Usage (Browser — Transformers.js)

import { pipeline } from '@huggingface/transformers';

const generator = await pipeline('text-generation', 'celestialcreator/axon-smollm2-360m', {
  dtype: 'q4f16',
  device: 'webgpu',
});

const result = await generator([
  { role: 'user', content: 'Who is Akshay?' }
], { max_new_tokens: 150 });

Usage (Python)

from transformers import pipeline

pipe = pipeline("text-generation", model="celestialcreator/axon-smollm2-360m")
result = pipe([{"role": "user", "content": "Who is Akshay?"}], max_new_tokens=150)

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Safetensors

Model size

0.4B params

Tensor type

F16

Model tree for celestialcreator/axon-smollm2-360m

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Adapter

(35)

this model