Instructions to use P0u4a/t0-mt-3b-base-philosophy-sdf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use P0u4a/t0-mt-3b-base-philosophy-sdf with Transformers:

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

pipe = pipeline("text-generation", model="P0u4a/t0-mt-3b-base-philosophy-sdf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("P0u4a/t0-mt-3b-base-philosophy-sdf")
model = AutoModelForCausalLM.from_pretrained("P0u4a/t0-mt-3b-base-philosophy-sdf", 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 P0u4a/t0-mt-3b-base-philosophy-sdf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "P0u4a/t0-mt-3b-base-philosophy-sdf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "P0u4a/t0-mt-3b-base-philosophy-sdf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/P0u4a/t0-mt-3b-base-philosophy-sdf

SGLang

How to use P0u4a/t0-mt-3b-base-philosophy-sdf 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 "P0u4a/t0-mt-3b-base-philosophy-sdf" \
    --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": "P0u4a/t0-mt-3b-base-philosophy-sdf",
		"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 "P0u4a/t0-mt-3b-base-philosophy-sdf" \
        --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": "P0u4a/t0-mt-3b-base-philosophy-sdf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use P0u4a/t0-mt-3b-base-philosophy-sdf with Docker Model Runner:
```
docker model run hf.co/P0u4a/t0-mt-3b-base-philosophy-sdf
```

Philosophy SDF midtrained 3B base

Derived from dlab-spp/t0-mt-3b-base, step-0 commit 8fa2bf935619631864c76e86e172202d94729e09.

One complete epoch over 13,201 decontaminated philosophy documents: 42,928,718 next-token targets, 20,972 packed blocks. LoRA rank 64, alpha 128 on attention and MLP projections; AdamW, learning rate 1e-4, cosine decay, 5% warmup, weight decay 0.01, context 2048. Follows the MSM method in https://arxiv.org/abs/2605.02087.

Root safetensors contain the merged, BF16 model. adapter/ also contains the LoRA adapter for the pinned base. See training_report.json for exact provenance, metrics, and coverage.

Downloads last month: 450

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for P0u4a/t0-mt-3b-base-philosophy-sdf

Base model

dlab-spp/t0-mt-3b-base

Adapter

(1)

this model

Dataset used to train P0u4a/t0-mt-3b-base-philosophy-sdf

Paper for P0u4a/t0-mt-3b-base-philosophy-sdf

Model Spec Midtraining: Improving How Alignment Training Generalizes

Paper • 2605.02087 • Published May 3