Instructions to use dlab-spp/vanilla-1.7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dlab-spp/vanilla-1.7b-base with Transformers:

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

pipe = pipeline("text-generation", model="dlab-spp/vanilla-1.7b-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dlab-spp/vanilla-1.7b-base")
model = AutoModelForCausalLM.from_pretrained("dlab-spp/vanilla-1.7b-base", 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 dlab-spp/vanilla-1.7b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dlab-spp/vanilla-1.7b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dlab-spp/vanilla-1.7b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dlab-spp/vanilla-1.7b-base

SGLang

How to use dlab-spp/vanilla-1.7b-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 "dlab-spp/vanilla-1.7b-base" \
    --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": "dlab-spp/vanilla-1.7b-base",
		"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 "dlab-spp/vanilla-1.7b-base" \
        --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": "dlab-spp/vanilla-1.7b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dlab-spp/vanilla-1.7b-base with Docker Model Runner:
```
docker model run hf.co/dlab-spp/vanilla-1.7b-base
```

Vanilla — Base (1.7B)

Type: base (pretrained) model. Not instruction-tuned and ships no chat template.

Baseline. Standard next-token pretraining on the full data mixture, with no pretraining safety intervention.

Instruction-tuned counterpart: dlab-spp/vanilla-1.7b-instruct.

Model details

Architecture: SmolLM2-1.7B architecture, trained from scratch.
Tokenizer: the original SmolLM2 tokenizer (vocabulary 49152).
Pretraining: ~100B tokens on a subset of the Olmo 3 Dolma 3 mixture.

Training checkpoints

Intermediate checkpoints are published as git revisions on this repo, so any point in the trajectory can be loaded by passing revision=:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "dlab-spp/vanilla-1.7b-base"
tok = AutoTokenizer.from_pretrained(repo)          # identical at every revision
model = AutoModelForCausalLM.from_pretrained(
    repo, revision="step-5000", dtype=torch.bfloat16, device_map="auto"
)

Revision	Pretraining step	Tokens seen	LR phase
`step-5000`	5,000 / 50,863	~9.8B	stable
`step-10000`	10,000 / 50,863	~19.7B	stable
`step-15000`	15,000 / 50,863	~29.5B	stable
`step-20000`	20,000 / 50,863	~39.3B	stable
`step-25000`	25,000 / 50,863	~49.2B	stable
`step-30000`	30,000 / 50,863	~59.0B	stable
`step-35000`	35,000 / 50,863	~68.8B	stable
`step-40000`	40,000 / 50,863	~78.6B	stable
`step-45000`	45,000 / 50,863	~88.5B	stable
`step-50863`	50,863 / 50,863	~100B	linear decay — same weights as `main`

main always holds the finished model (step 50,863). Only model weights are published — optimizer and RNG state are not included, so these revisions support evaluation, probing, and fine-tuning, but not exact resumption of the original run.

Intended use

Research on alignment and safety. As a base model it is meant for continuation, probing, or further fine-tuning; it is not instruction-tuned and can produce incorrect or unsafe content.

Citation

@misc{minder2026syntheticpersonapretrainingalignment,
      title={Synthetic Persona Pretraining: Alignment from Token Zero},
      author={Julian Minder and Viktor Moskvoretskii and Raghav Singhal and Difan Jiao and Andy Arditi and Shaobo Cui and Yiderigun Borjigin and Kartik Bali and Stefan Krsteski and Harsh Raj and Huu Nguyen and Jannik Brinkmann and Ashton Anderson and Roland Aydin and Robert West},
      year={2026},
      eprint={2608.13482},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2608.13482},
}

License: to be finalised.

Downloads last month: 550

Safetensors

Model size

2B params

Tensor type

BF16

Collection including dlab-spp/vanilla-1.7b-base

Models - 1.7B

Collection

1.7B SPP models: bases publish their training trajectory as step-* revisions, plus persona-binding instruct counterparts. • 10 items • Updated 22 days ago

Paper for dlab-spp/vanilla-1.7b-base

Synthetic Persona Pretraining: Alignment from Token Zero

Paper • 2608.13482 • Published 9 days ago

dlab-spp
/

vanilla-1.7b-base