Instructions to use dwidlee/systemone-lite-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dwidlee/systemone-lite-0.5b with Transformers:

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

pipe = pipeline("text-generation", model="dwidlee/systemone-lite-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dwidlee/systemone-lite-0.5b")
model = AutoModelForCausalLM.from_pretrained("dwidlee/systemone-lite-0.5b", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dwidlee/systemone-lite-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dwidlee/systemone-lite-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dwidlee/systemone-lite-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dwidlee/systemone-lite-0.5b

SGLang

How to use dwidlee/systemone-lite-0.5b 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 "dwidlee/systemone-lite-0.5b" \
    --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": "dwidlee/systemone-lite-0.5b",
		"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 "dwidlee/systemone-lite-0.5b" \
        --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": "dwidlee/systemone-lite-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dwidlee/systemone-lite-0.5b with Docker Model Runner:
```
docker model run hf.co/dwidlee/systemone-lite-0.5b
```

systemone-lite-0.5b

Fine-tuned weights for systemone-lite: typed decisions via next-token scoring over option aliases (choice / encoded noul / score).

Not affiliated with TypeSafe AI or Jev.

Item	Value
Base	`Qwen/Qwen2.5-0.5B-Instruct` (Apache-2.0)
Data	`dwidlee/systemone-lite-general`
Train size	32 400 rows (ticket / alloc / debate)
Recipe	1 epoch, batch size 3, gym-stratified batches, 10 800 steps
Loss	Cross-entropy on the labeled option alias token

Chess-specialized weights are a separate local checkpoint; this model is not trained on chess.

Accuracy (option top-1)

Split	n	Base 0.5B	This model	Δ
iid (`test`)	3600	0.439	0.679	+0.240
hard (`test_hard`)	5400	0.427	0.652	+0.225

Hard: alternate state layouts, option subsets, paraphrases (same label rules). Repo reports: benchmarks/general_*_eval*.json.

Selected iid (this model): ticket.route 1.000, alloc.fund_next 0.980, ticket.needs_human 0.775. Near base: debate.winner 0.493, debate.enough_evidence 0.460.

Chess transfer (same eval harness)

Move top-1 on chess_eval_5k (n=500): base 0.790, this checkpoint 0.458, chess-only SFT 0.834. General SFT does not improve chess.

Latency (inference path; base 0.5B measured)

In-process on RTX 3060, warmup excluded. Same scoring path this checkpoint uses (0.5B forward dominates latency).

Option scoring vs AR JSON (model.generate greedy multi-field JSON, full vocab). Source: repo benchmarks/latency_vs_ar.json.

Case	Option p50 (ms)	AR JSON p50 (ms)	AR / option
short_3q	26.2	1057	40.3×
short_13q	64.9	3482	53.7×
long_3q (~6k chars)	107.6	1137	10.6×
long_13q	157.9	3613	22.9×

Option path: batched next-token logits; softmax over option token ids; prefix KV. AR runs often used the full max_new_tokens budget (no early EOS). Option path is schema-constrained; AR JSON validity is not guaranteed in this bench.

TypeSafe public materials cite Jev E2E roughly 70–500 ms (cloud + network; not measured here).

Inference method (server)

Encode shared state once (prefix KV).
Batch per-question suffixes.
Softmax only over criteria / yes–no / score-level token ids.
Assemble System One–shaped answers.

systemone-lite --model dwidlee/systemone-lite-0.5b --port 8000

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dwidlee/systemone-lite-0.5b"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

Limits

Synthetic rule labels; not human preference data or RLCD.
Option softmax ≠ population calibration (no ECE curves published).
Multi-token option strings are not first-class (training uses letter aliases).

Downloads last month: 499

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for dwidlee/systemone-lite-0.5b

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(1030)

this model

dwidlee
/

systemone-lite-0.5b