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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("epfl-dlab/spp-vanilla-3b-instruct")
model = AutoModelForCausalLM.from_pretrained("epfl-dlab/spp-vanilla-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Raghav-Singhal commited on Jul 29

Commit

e5913b3

verified ·

1 Parent(s): 2ce5d7c

Update org/repo references after transfer

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ tags:
 Baseline (no pretraining safety intervention), post-trained with the shared persona-binding SFT.
-Base counterpart: [`model-raising/vanilla-3b-base`](https://huggingface.co/model-raising/vanilla-3b-base).
 ## Model details
 - **Architecture:** Llama-3.2-3B-shaped, trained from scratch.
@@ -33,7 +33,7 @@ There is **no system prompt**. Each assistant turn opens with `<|im_start|><assi
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-repo = "model-raising/vanilla-3b-instruct"
 tok = AutoTokenizer.from_pretrained(repo)
 model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")
@@ -48,6 +48,6 @@ Research on alignment and safety (constitutional alignment, value generalization
 ## Links
 - Paper: _to be released_
-- Collection: https://huggingface.co/collections/model-raising/spp
-_License: to be finalised before this repo is made public._

 Baseline (no pretraining safety intervention), post-trained with the shared persona-binding SFT.
+Base counterpart: [`epfl-dlab/spp-vanilla-3b-base`](https://huggingface.co/epfl-dlab/spp-vanilla-3b-base).
 ## Model details
 - **Architecture:** Llama-3.2-3B-shaped, trained from scratch.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+repo = "epfl-dlab/spp-vanilla-3b-instruct"
 tok = AutoTokenizer.from_pretrained(repo)
 model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")
 ## Links
 - Paper: _to be released_
+- Collection: https://huggingface.co/collections/epfl-dlab/spp
+_License: to be finalised._