Instructions to use dlab-spp/t0-1.7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dlab-spp/t0-1.7b-base with Transformers:

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

pipe = pipeline("text-generation", model="dlab-spp/t0-1.7b-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dlab-spp/t0-1.7b-base")
model = AutoModelForCausalLM.from_pretrained("dlab-spp/t0-1.7b-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/t0-1.7b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dlab-spp/t0-1.7b-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/t0-1.7b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dlab-spp/t0-1.7b-base

SGLang

How to use dlab-spp/t0-1.7b-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/t0-1.7b-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/t0-1.7b-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/t0-1.7b-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/t0-1.7b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dlab-spp/t0-1.7b-base with Docker Model Runner:
```
docker model run hf.co/dlab-spp/t0-1.7b-base
```

t0-1.7b-base

File size: 3,070 Bytes

---
license: other
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- spp
- synthetic-persona-pretraining
- spp
- alignment
- safety
---

# SPP-T0 — Base (1.7B)

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

Trained with Synthetic Persona Pretraining (SPP) from token zero: first-person reflections are inserted into the roughly 10% of annotated documents that carry one, throughout the entire pretraining run.

## Synthetic Persona Pretraining (SPP)
**Synthetic Persona Pretraining (SPP)** installs a target value persona during pretraining rather than only during alignment. Value-laden, first-person reflections, generated against a constitution, are appended to a subset of pretraining documents after a special `<assistant>` token. Attention masking and RoPE position aliasing keep the reflection from changing the continuation of the original document. This model is trained with SPP.

Instruction-tuned counterpart: [`dlab-spp/t0-1.7b-instruct`](https://huggingface.co/dlab-spp/t0-1.7b-instruct).

## Model details
- **Architecture:** SmolLM2-1.7B architecture, trained from scratch.
- **Tokenizer:** the SmolLM2 tokenizer extended with an `<assistant>` marker and constitution tokens (vocabulary 49280).
- **Pretraining:** ~100B tokens on a subset of the Olmo 3 Dolma 3 mixture, with SPP reflections inserted into the safety-annotated documents within it.

## Training checkpoints

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

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "dlab-spp/t0-1.7b-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.

## Links
- Paper: _to be released_

_License: to be finalised._