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

Libraries

How to use trychoosing/orbit-drafter-smollm2-360m with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trychoosing/orbit-drafter-smollm2-360m")
model = AutoModelForCausalLM.from_pretrained("trychoosing/orbit-drafter-smollm2-360m", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trychoosing/orbit-drafter-smollm2-360m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trychoosing/orbit-drafter-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": "trychoosing/orbit-drafter-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trychoosing/orbit-drafter-smollm2-360m

SGLang

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

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

Orbit Support Drafter (SFT)

The Drafter agent of a LangGraph multi-agent support system for the fictional shop Orbit Electronics. Base model HuggingFaceTB/SmolLM2-360M-Instruct, LoRA-fine-tuned and merged. Stage: sft (sft = supervised on the response simulator; dpo = then aligned with human and critic preferences via Direct Preference Optimisation).

Evaluation (held-out simulated tickets, full multi-agent graph)

stage	temperature	first-draft approval	resolved	avg drafts
base	0	0	0	3
sft	0	0.619	0.619	1.762
sft	0.7	0.476	0.524	2

Knowledge-base fingerprint: 034b279610ce. Demo model trained on synthetic data; it only knows the toy policies of Orbit Electronics.

Downloads last month: 177

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for trychoosing/orbit-drafter-smollm2-360m

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Adapter

(71)

this model