Instructions to use 169Pi/Alpie_seed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 169Pi/Alpie_seed with Transformers:

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

pipe = pipeline("image-text-to-text", model="169Pi/Alpie_seed")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("169Pi/Alpie_seed")
model = AutoModelForMultimodalLM.from_pretrained("169Pi/Alpie_seed", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 169Pi/Alpie_seed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "169Pi/Alpie_seed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "169Pi/Alpie_seed",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/169Pi/Alpie_seed

SGLang

How to use 169Pi/Alpie_seed 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 "169Pi/Alpie_seed" \
    --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": "169Pi/Alpie_seed",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "169Pi/Alpie_seed" \
        --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": "169Pi/Alpie_seed",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use 169Pi/Alpie_seed with Docker Model Runner:
```
docker model run hf.co/169Pi/Alpie_seed
```

deepanshupillm commited on 1 day ago

Commit

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Update README.md

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@@ -91,6 +91,15 @@ Alpie Seed uses a two-stage post-training pipeline.
 Training used eight NVIDIA H100 GPUs, with 3,034 GRPO steps across five phases. The reward combines seven signals: answer correctness, hallucination penalty, self-consistency, educational quality, structure preservation, repetition penalty, and length penalty.
 <br>
 <div align="center">
@@ -107,37 +116,37 @@ Training used eight NVIDIA H100 GPUs, with 3,034 GRPO steps across five phases.
 </div>
-> Alpie Seed scores are from the FP16 model and do not apply to the 2-bit, 3-bit, or 4-bit variants. 🏆 gold badge = best score in the row; purple badge = Alpie Seed score. Bold 🏆 = best score in the row. Comparison-model scores are taken from published model cards and reports (including the [Qwen3.5-9B model card](https://huggingface.co/Qwen/Qwen3.5-9B)).
 ### 🧮 Language
 | | Qwen3.5-9B | Gemma 4 12B | Ministral 3 8B | GPT-OSS-20B | Sarvam-30B | Nemotron 3.5 Lightning | **Alpie Seed 9B** |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 | **Knowledge & STEM** | | | | | | | |
-| MMLU | -- | -- | -- | 85.3 | 85.1 | -- | ![87.30](https://img.shields.io/badge/87.30-F59E0B?style=flat-square) 🏆 |
-| MMLU-Pro | 82.5 | 77.2 | 75.00 | 77.2 | 80.0 | 81.94 | ![82.54](https://img.shields.io/badge/82.54-F59E0B?style=flat-square) 🏆 |
-| GPQA Diamond | 81.7 | 78.8 | 38.6 | 71.5 | 66.5 | 75.44 | ![84.32](https://img.shields.io/badge/84.32-F59E0B?style=flat-square) 🏆 |
 | **Mathematics** | | | | | | | |
 | GSM8K | -- | -- | -- | -- | -- | -- | ![98.00](https://img.shields.io/badge/98.00-7C3AED?style=flat-square) |
-| Math-500 | -- | -- | -- | 94.2 | **97.0** 🏆 | -- | ![91.00](https://img.shields.io/badge/91.00-7C3AED?style=flat-square) |
-| AIME 2026 | 69.4 | 77.5 | -- | -- | -- | -- | ![83.33](https://img.shields.io/badge/83.33-F59E0B?style=flat-square) 🏆 |
 | **Coding & Software Engineering** | | | | | | | |
-| LiveCodeBench v6 | 65.6 | 72.0 | 61.6 | 61.0 | 70.0 | -- | ![72.99](https://img.shields.io/badge/72.99-F59E0B?style=flat-square) 🏆 |
-| SWE-Bench Verified | 60.0 | 52.0 | -- | 60.7 | 34.0 | 51.56 | ![61.20](https://img.shields.io/badge/61.20-F59E0B?style=flat-square) 🏆 |
 ### 👁️ Vision Language
 | | GPT-5-Nano-2025-08-07 | Gemini-2.5-Flash-Lite | Qwen3-VL-30B-A3B | Qwen3.5-9B | Gemma 4 12B | **Alpie Seed 9B** |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|
 | **STEM and Puzzle** | | | | | | |
-| MMMU-Pro | 57.2 | 59.7 | 63.0 | **70.1** 🏆 | 69.1 | ![68.70](https://img.shields.io/badge/68.70-7C3AED?style=flat-square) |
-| MathVision | 62.2 | 52.1 | 65.7 | 78.9 | **79.7** 🏆 | ![76.76](https://img.shields.io/badge/76.76-7C3AED?style=flat-square) |
 | **General VQA** | | | | | | |
-| RealWorldQA | 71.8 | 72.2 | 77.4 | 80.3 | -- | ![80.39](https://img.shields.io/badge/80.39-F59E0B?style=flat-square) 🏆 |
-| MMStar | 68.6 | 69.1 | 75.5 | **79.7** 🏆 | -- | ![78.87](https://img.shields.io/badge/78.87-7C3AED?style=flat-square) |
-| HallusionBench | 58.4 | 64.5 | 66.0 | 69.3 | -- | ![79.88](https://img.shields.io/badge/79.88-F59E0B?style=flat-square) 🏆 |
 | **Text Recognition and Document Understanding** | | | | | | |
-| OCRBench | 75.3 | 82.5 | 83.9 | 89.2 | -- | ![90.30](https://img.shields.io/badge/90.30-F59E0B?style=flat-square) 🏆 |
 * SWE-Bench Verified: resolved rate using Mini-SWE-agent, with retries disabled.
 * Comparison-model scores were not re-run by 169Pi, and harnesses, prompts, and benchmark versions differ, so cross-model comparisons are indicative rather than strictly like-for-like. Scores vary with dataset versions, prompts, evaluation harnesses, and inference settings.
@@ -155,7 +164,7 @@ vllm serve 169Pi/Alpie_seed --port 8000 --max-model-len 262144 --reasoning-parse
 Run locally with Ollama:
 ```shell
-ollama run 169pi/alpie-core
 ```
 Quantized 2-bit, 3-bit, and 4-bit builds are in the [GGUF repository](https://huggingface.co/169Pi/Alpie_Seed_GGUF). Thinking and non-thinking behavior is controlled through the chat template.

 Training used eight NVIDIA H100 GPUs, with 3,034 GRPO steps across five phases. The reward combines seven signals: answer correctness, hallucination penalty, self-consistency, educational quality, structure preservation, repetition penalty, and length penalty.
+## Links
+- GitHub: https://github.com/169Pi
+- LinkedIn: https://www.linkedin.com/company/169pi
+- Instagram: https://www.instagram.com/169pi.ai/
+- YouTube: https://www.youtube.com/@169pi
+- X: https://x.com/169Pi_ai
+- Ollama: https://ollama.com/169pi
 <br>
 <div align="center">
 </div>
+> Alpie Seed scores are from the FP16 model and do not apply to the 2-bit, 3-bit, or 4-bit variants. Gold badge = best score in the row; purple badge = Alpie Seed score. Bold = best score in the row. Comparison-model scores are taken from published model cards and reports (including the [Qwen3.5-9B model card](https://huggingface.co/Qwen/Qwen3.5-9B)).
 ### 🧮 Language
 | | Qwen3.5-9B | Gemma 4 12B | Ministral 3 8B | GPT-OSS-20B | Sarvam-30B | Nemotron 3.5 Lightning | **Alpie Seed 9B** |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 | **Knowledge & STEM** | | | | | | | |
+| MMLU | -- | -- | -- | 85.3 | 85.1 | -- | ![87.30](https://img.shields.io/badge/87.30-F59E0B?style=flat-square) |
+| MMLU-Pro | 82.5 | 77.2 | 75.00 | 77.2 | 80.0 | 81.94 | ![82.54](https://img.shields.io/badge/82.54-F59E0B?style=flat-square) |
+| GPQA Diamond | 81.7 | 78.8 | 38.6 | 71.5 | 66.5 | 75.44 | ![84.32](https://img.shields.io/badge/84.32-F59E0B?style=flat-square) |
 | **Mathematics** | | | | | | | |
 | GSM8K | -- | -- | -- | -- | -- | -- | ![98.00](https://img.shields.io/badge/98.00-7C3AED?style=flat-square) |
+| Math-500 | -- | -- | -- | 94.2 | **97.0** | -- | ![91.00](https://img.shields.io/badge/91.00-7C3AED?style=flat-square) |
+| AIME 2026 | 69.4 | 77.5 | -- | -- | -- | -- | ![83.33](https://img.shields.io/badge/83.33-F59E0B?style=flat-square) |
 | **Coding & Software Engineering** | | | | | | | |
+| LiveCodeBench v6 | 65.6 | 72.0 | 61.6 | 61.0 | 70.0 | -- | ![72.99](https://img.shields.io/badge/72.99-F59E0B?style=flat-square) |
+| SWE-Bench Verified | 60.0 | 52.0 | -- | 60.7 | 34.0 | 51.56 | ![61.20](https://img.shields.io/badge/61.20-F59E0B?style=flat-square) |
 ### 👁️ Vision Language
 | | GPT-5-Nano-2025-08-07 | Gemini-2.5-Flash-Lite | Qwen3-VL-30B-A3B | Qwen3.5-9B | Gemma 4 12B | **Alpie Seed 9B** |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|
 | **STEM and Puzzle** | | | | | | |
+| MMMU-Pro | 57.2 | 59.7 | 63.0 | **70.1** | 69.1 | ![68.70](https://img.shields.io/badge/68.70-7C3AED?style=flat-square) |
+| MathVision | 62.2 | 52.1 | 65.7 | 78.9 | **79.7** | ![76.76](https://img.shields.io/badge/76.76-7C3AED?style=flat-square) |
 | **General VQA** | | | | | | |
+| RealWorldQA | 71.8 | 72.2 | 77.4 | 80.3 | -- | ![80.39](https://img.shields.io/badge/80.39-F59E0B?style=flat-square) |
+| MMStar | 68.6 | 69.1 | 75.5 | **79.7** | -- | ![78.87](https://img.shields.io/badge/78.87-7C3AED?style=flat-square) |
+| HallusionBench | 58.4 | 64.5 | 66.0 | 69.3 | -- | ![79.88](https://img.shields.io/badge/79.88-F59E0B?style=flat-square) |
 | **Text Recognition and Document Understanding** | | | | | | |
+| OCRBench | 75.3 | 82.5 | 83.9 | 89.2 | -- | ![90.30](https://img.shields.io/badge/90.30-F59E0B?style=flat-square) |
 * SWE-Bench Verified: resolved rate using Mini-SWE-agent, with retries disabled.
 * Comparison-model scores were not re-run by 169Pi, and harnesses, prompts, and benchmark versions differ, so cross-model comparisons are indicative rather than strictly like-for-like. Scores vary with dataset versions, prompts, evaluation harnesses, and inference settings.
 Run locally with Ollama:
 ```shell
+ollama run 169pi/alpie_seed
 ```
 Quantized 2-bit, 3-bit, and 4-bit builds are in the [GGUF repository](https://huggingface.co/169Pi/Alpie_Seed_GGUF). Thinking and non-thinking behavior is controlled through the chat template.