Instructions to use epfl-dlab/spp-t0-3b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use epfl-dlab/spp-t0-3b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="epfl-dlab/spp-t0-3b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("epfl-dlab/spp-t0-3b-instruct")
model = AutoModelForCausalLM.from_pretrained("epfl-dlab/spp-t0-3b-instruct", 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 epfl-dlab/spp-t0-3b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "epfl-dlab/spp-t0-3b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epfl-dlab/spp-t0-3b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/epfl-dlab/spp-t0-3b-instruct

SGLang

How to use epfl-dlab/spp-t0-3b-instruct 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 "epfl-dlab/spp-t0-3b-instruct" \
    --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": "epfl-dlab/spp-t0-3b-instruct",
		"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 "epfl-dlab/spp-t0-3b-instruct" \
        --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": "epfl-dlab/spp-t0-3b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use epfl-dlab/spp-t0-3b-instruct with Docker Model Runner:
```
docker model run hf.co/epfl-dlab/spp-t0-3b-instruct
```

Raghav-Singhal commited on Jul 22

Commit

60b9746

verified ·

1 Parent(s): b794aa1

Note SPP-annotated docs on the pretraining line

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -26,7 +26,7 @@ One of the five 3B instruction-tuned models from **Synthetic Persona Pretraining
 ## Model details
 - **Architecture:** Llama-3.2-3B-shaped dense decoder-only transformer (28 layers, hidden size 3072, 24 attention heads, 8 KV groups, RoPE, RMSNorm, SwiGLU, tied input/output embeddings), trained from scratch.
 - **Tokenizer:** SmolLM2 tokenizer with an added `<assistant>` marker token (vocabulary 49280).
-- **Pretraining:** ~500B tokens on a subset of the Olmo 3 Dolma 3 mixture.
 - **Post-training:** persona-binding supervised fine-tuning (PBSFT-mix): 300k single-turn examples, 90% WildChat-1M instructions and 10% safety prompts (WildJailbreak, WildGuardMix); assistant responses follow the Model Raising Constitution with inline `[N.M]` citations; response-only loss, one epoch.
 ## Chat format

 ## Model details
 - **Architecture:** Llama-3.2-3B-shaped dense decoder-only transformer (28 layers, hidden size 3072, 24 attention heads, 8 KV groups, RoPE, RMSNorm, SwiGLU, tied input/output embeddings), trained from scratch.
 - **Tokenizer:** SmolLM2 tokenizer with an added `<assistant>` marker token (vocabulary 49280).
+- **Pretraining:** ~500B tokens on a subset of the Olmo 3 Dolma 3 mixture, with SPP reflections inserted into the safety-annotated documents within it.
 - **Post-training:** persona-binding supervised fine-tuning (PBSFT-mix): 300k single-turn examples, 90% WildChat-1M instructions and 10% safety prompts (WildJailbreak, WildGuardMix); assistant responses follow the Model Raising Constitution with inline `[N.M]` citations; response-only loss, one epoch.
 ## Chat format