Alpie Seed 9B GGUF

Run Alpie Seed locally.

Alpie Seed running fully offline on a phone, tested in PocketPal.

alpie_offline_mobile_demo.mp4

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About Alpie Seed GGUF

This repository provides GGUF quantized versions of Alpie Seed 9B, the open-weight vision-language model developed by 169Pi AI.

Designed for local deployment, these releases make Alpie Seed accessible across desktops, workstations, and supported mobile hardware. Multiple quantization levels allow developers and researchers to choose the right balance between model size, memory requirements, inference performance, and output quality.

Highlights

• 9B Parameters: Based on Alpie Seed 9B.
• Multiple Quantizations: 2-bit, 3-bit, and 4-bit options.
• Lower Perplexity than Qwen: In our perplexity comparison, Alpie Seed scores lower (better) than Qwen at both BF16 and Q4. See Perplexity: Alpie Seed vs Qwen.
• Local Inference: Run the model on compatible hardware.
• Vision-Language: Image understanding and visual reasoning.
• Reasoning and Coding: Mathematical problem-solving, code generation, and debugging.
• Long Context: Native 262K context window in the base model.
• Open License: Apache License 2.0.

Available On

Alpie Seed is also available on the following platforms:

Ollama

Run Alpie Seed directly from the Ollama library:

ollama run 169pi/alpie-seed

Available tags:

To run a specific tag:

ollama run 169pi/alpie-seed:q2
ollama run 169pi/alpie-seed:vision

Kaggle

Alpie Seed 9B is available on Kaggle Models: https://www.kaggle.com/models/alpie/alpie-seed-9b

Quantization Formats

Different quantization levels provide different trade-offs between model size, memory requirements, inference performance, and output quality.

Actual file sizes and memory requirements depend on the specific GGUF variant, context length, and runtime configuration.

Refer to the repository file list to select the appropriate model.

Capabilities

Alpie Seed inherits the capabilities of its base model, including:

• Multi-step reasoning and problem-solving
• Mathematical reasoning
• Code generation and debugging
• Technical explanations and assistance
• Image understanding and visual reasoning
• OCR and document understanding
• Chart and visual information interpretation
• Long-context processing up to 262K tokens

The 262K context window is the native context capability of the base model. Actual usable context depends on runtime support, configuration, and available memory.

Multimodal functionality requires a compatible runtime and the corresponding vision components.

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Quickstart

1. Download the Model

Install the Hugging Face CLI:

pip install -U huggingface_hub

Download the repository:

hf download 169Pi/Alpie_Seed_GGUF --local-dir ./Alpie_Seed_GGUF

2. Select Your Quantization

Navigate to the downloaded directory:

cd Alpie_Seed_GGUF
ls -lh

Select the Q2, Q3, or Q4 GGUF file according to your available memory.

3. Run with llama.cpp

Install llama.cpp using its official installation instructions:

https://github.com/ggml-org/llama.cpp

For basic text generation:

llama-cli \
  -m ./your-selected-model.gguf \
  -p "Explain the difference between supervised fine-tuning and reinforcement learning." \
  -n 256

Replace your-selected-model.gguf with the actual model filename.

For interactive conversation:

llama-cli \
  -m ./your-selected-model.gguf \
  -cnv

GPU Acceleration

For NVIDIA GPU inference, enable GPU layer offloading:

llama-cli \
  -m ./your-selected-model.gguf \
  -ngl 99 \
  -cnv

Adjust the number of GPU layers according to available VRAM.

For CPU-only inference:

llama-cli \
  -m ./your-selected-model.gguf \
  -ngl 0 \
  -cnv

4. Multimodal Inference (Image Understanding)

Alpie Seed supports vision inference using a compatible multimodal llama.cpp build and its corresponding vision projector.

Download the compatible vision projector from the repository if provided.

Example:

llama-cli \
  -m ./your-selected-model.gguf \
  --mmproj ./alpie-mmproj-f16.gguf \
  --image ./example.jpg \
  -p "Describe the contents of this image."

The language model and vision projector must be compatible with each other.

Refer to the llama.cpp multimodal documentation for additional configuration.

5. Run with Ollama

Install Ollama:

https://ollama.com/download

To run the hosted model directly from the Ollama library, see Available On above:

ollama run 169pi/alpie-seed

Alternatively, to import a local GGUF file, create a file named Modelfile in your model directory:

FROM ./your-selected-model.gguf

PARAMETER temperature 0.7
PARAMETER num_ctx 8192

Import the model:

ollama create alpie-seed -f Modelfile

Run it:

ollama run alpie-seed

Verify the imported model:

ollama show alpie-seed

For additional information, see the Ollama GGUF import documentation.

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Perplexity: Alpie Seed vs Qwen

What is perplexity (PPL)?

Perplexity measures how well a language model predicts text it is shown. For every word (token) in a passage, the model assigns a probability to what comes next. If it gave a high probability to the word that actually came next, it was not "surprised." Perplexity averages that surprise across the whole passage.

A handy way to read it: perplexity is roughly the number of equally likely next words the model is torn between at each step. A PPL of 9 means the model is, on average, about as uncertain as if it were picking between 9 equally likely options.

Why is lower better?

• Lower PPL = fewer choices = more confident, more accurate predictions of real text.
• Higher PPL = more choices = the model is more confused by the text.

The graph below shows the idea on a toy example. Both models see "The capital of France is ___". The model on the left puts most of its probability on the right answer (low perplexity). The model on the right spreads its probability across many words (high perplexity).

Prompt: "The capital of France is ___"   (illustrative example)

LOW perplexity: model is confident
  Paris   ████████████████████████████████████ 0.86
  London  █                                    0.03
  Rome    █                                    0.03
  Berlin  █                                    0.02
  Madrid  █                                    0.02
  Tokyo   █                                    0.02
  Cairo   █                                    0.01
  Delhi   █                                    0.01

HIGH perplexity: model is unsure
  Paris   ████████                             0.20
  London  ███████                              0.16
  Rome    ██████                               0.14
  Berlin  █████                                0.13
  Madrid  █████                                0.12
  Tokyo   ████                                 0.10
  Cairo   ███                                  0.08
  Delhi   ███                                  0.07

Results

We compared mean perplexity for Alpie Seed and Qwen at two precisions: BF16 (full-precision reference) and Q4 (4-bit quantized).

Mean perplexity (lower is better)   [bars start at 8.75, not 0]

BF16 (full precision)
  Qwen       ████████████████████████████████████████ 9.0251
  Alpie Seed █████████████████████████████████████    9.0065   <- 0.21% lower (better)

Q4 (4-bit quantized)
  Qwen       ███████████████████████████████████████  9.0215
  Alpie Seed █████████████████████                    8.8921   <- 1.43% lower (better)

Takeaway: Alpie Seed reaches a lower perplexity than Qwen at BF16 and at Q4, so it predicts the evaluation text more confidently at both precisions. The gap is small at BF16 and larger at Q4.

How to read this fairly

• The bars in the chart start at 8.75, not 0, so small differences are visible. The differences are real but modest, as the percentages in the table show.
• Compare Alpie against Qwen at the same precision. The Alpie Q4 score (8.8921) is lower than its own BF16 reference (9.0065). Quantization normally keeps perplexity the same or raises it slightly, so this likely reflects that the BF16 reference checkpoint may not match the checkpoint the Q4 file was built from. It should not be read as "Q4 is better than BF16."
• Perplexity is not a task benchmark. It measures next-token prediction on a specific text set. It does not by itself prove better reasoning, coding, or instruction following.
• Perplexity values are only comparable when the text and settings are the same. The numbers in this section are not comparable to the separate NF4 retention numbers further below, which were measured on a different corpus.

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Model Evaluation Benchmarks

The following results summarize the reported Alpie Seed evaluation measurements. Results are separated by precision and evaluation configuration to avoid mixing model evaluation with standalone GGUF serving performance.

Evaluation Metrics Summary

Inference Performance

Reported BF16 benchmark results

Measurement notes:

• Generation speed is an end-to-end measurement.
• TTFT is measured from the first streamer output and is not a pure model-kernel TTFT.
• The 17.555 GB figure represents the reported model snapshot size, not a verified weights-only file size.

Quantization Retention

BF16 versus NF4 evaluation on a 295,893-token text corpus

Additional quantization measurements:

These results indicate the measured retention characteristics of the NF4 configuration relative to BF16 on the specified evaluation corpus. Here, a perplexity increase of 4.13% means the 4-bit model is only slightly less confident than the full-precision model while using about 59% less memory.

GPU Memory and Long-Context Evaluation

Reported BF16 memory and latency measurements

The 32K evaluation completed successfully.

Context window: Alpie Seed has a native 262K-token context window. The measurements above cover up to 32K tokens; the full 262K context was not tested in this particular memory evaluation.

Benchmark Interpretation

• BF16 measurements represent the reported inference benchmark configuration.
• NF4 peak GPU memory of 25.379 GB comes from a combined two-model evaluation and must not be interpreted as standalone NF4 serving memory.
• Quantization retention measurements are separate from the Q2, Q3, and Q4 GGUF releases.
• The Alpie Seed vs Qwen perplexity comparison is a separate run from the NF4 retention measurements and uses different text, so the two sets of perplexity values should not be compared with each other.
• The measurements should not be interpreted as universal performance guarantees for every device or runtime.

For the complete model evaluation and additional benchmark information, visit the primary Alpie Seed model card.

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Local Deployment and Intended Use

Alpie Seed GGUF is designed for:

• Local experimentation and research.
• Offline and private on-device workflows.
• Coding and reasoning assistants.
• Multimodal application development.
• Edge AI and resource-constrained deployment experiments.
• Evaluation of quantization and inference trade-offs.

Limitations

• Quantization can affect model quality and output consistency.
• Performance varies significantly across hardware and runtime configurations.
• Long-context capability depends on runtime support and available memory.
• Vision functionality requires compatible multimodal runtime support and the corresponding model assets.
• Mobile performance depends on memory availability, inference backend, and thermal constraints.
• Lower perplexity indicates better next-token prediction on the evaluation text, not guaranteed better performance on every task.
• Outputs should be independently validated, particularly in high-impact applications.

License

Released under the Apache License 2.0, consistent with the primary Alpie Seed model.

Citation

If you use Alpie Seed in your research or projects, please cite:

@misc{alpie_seed_9b_2026,
  title        = {Alpie Seed 9B},
  author       = {{169Pi AI}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/169Pi/Alpie_seed}}
}