Instructions to use dlab-spp/mt-3b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dlab-spp/mt-3b-base with Transformers:

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

pipe = pipeline("text-generation", model="dlab-spp/mt-3b-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dlab-spp/mt-3b-base")
model = AutoModelForCausalLM.from_pretrained("dlab-spp/mt-3b-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/mt-3b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dlab-spp/mt-3b-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/mt-3b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dlab-spp/mt-3b-base

SGLang

How to use dlab-spp/mt-3b-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/mt-3b-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/mt-3b-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/mt-3b-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/mt-3b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dlab-spp/mt-3b-base with Docker Model Runner:
```
docker model run hf.co/dlab-spp/mt-3b-base
```

mt-3b-base / README.md

jkminder

Align card with paper terminology (constitution/SPP) and update dlab-spp links

276640f verified 8 days ago

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

	# SPP-MT — Base (3B)

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

	The Vanilla model receives the same reflection-focused midtraining stage as SPP-T0-MT, so SPP reflections are introduced only at midtraining and never during the main 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/mt-3b-instruct`](https://huggingface.co/dlab-spp/mt-3b-instruct).

	## Model details
	- Architecture: Llama-3.2-3B-shaped, trained from scratch.
	- Tokenizer: the SmolLM2 tokenizer extended with an `<assistant>` marker and constitution tokens (vocabulary 49280).
	- Pretraining: ~500B tokens on a subset of the Olmo 3 Dolma 3 mixture; SPP reflections are applied only during a subsequent reflection-focused midtraining stage, on the safety-annotated documents.

	## 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/mt-3b-base"
	tok = AutoTokenizer.from_pretrained(repo) # identical at every revision
	model = AutoModelForCausalLM.from_pretrained(
	repo, revision="step-0", dtype=torch.bfloat16, device_map="auto"
	)
	```

	\| Revision \| Midtraining step \| LR phase \|
	\|---\|---\|---\|
	\| `step-0` \| 0 / 72,895 \| — (init from `vanilla-3b-base` step 225,000) \|
	\| `step-25000` \| 25,000 / 72,895 \| linear decay \|
	\| `step-50000` \| 50,000 / 72,895 \| linear decay \|
	\| `step-72895` \| 72,895 / 72,895 \| linear decay — same weights as `main` \|

	`main` always holds the finished model (step 72,895).
	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.

	Steps are counted from the start of midtraining. Midtraining resumed from pretraining step 225,000, so the earlier part of this model's history is the pretraining trajectory in [`dlab-spp/vanilla-3b-base`](https://huggingface.co/dlab-spp/vanilla-3b-base) (revisions `step-25000` … `step-225000`). Those checkpoints are shared and are not duplicated here; `step-0` is the exact fork point.

	## 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._