Instructions to use MatterWaveAI/grt-20m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatterWaveAI/grt-20m with Transformers:

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

pipe = pipeline("text-generation", model="MatterWaveAI/grt-20m")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MatterWaveAI/grt-20m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MatterWaveAI/grt-20m with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MatterWaveAI/grt-20m:F32
# Run inference directly in the terminal:
llama cli -hf MatterWaveAI/grt-20m:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MatterWaveAI/grt-20m:F32
# Run inference directly in the terminal:
llama cli -hf MatterWaveAI/grt-20m:F32

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf MatterWaveAI/grt-20m:F32
# Run inference directly in the terminal:
./build/bin/llama cli -hf MatterWaveAI/grt-20m:F32

Use Docker

docker model run hf.co/MatterWaveAI/grt-20m:F32

Llama new
LM Studio
Jan

vLLM

How to use MatterWaveAI/grt-20m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MatterWaveAI/grt-20m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatterWaveAI/grt-20m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MatterWaveAI/grt-20m:F32

SGLang

How to use MatterWaveAI/grt-20m 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 "MatterWaveAI/grt-20m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatterWaveAI/grt-20m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MatterWaveAI/grt-20m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatterWaveAI/grt-20m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use MatterWaveAI/grt-20m with Ollama:
```
ollama run hf.co/MatterWaveAI/grt-20m:F32
```
Unsloth Desktop
Docker Model Runner
How to use MatterWaveAI/grt-20m with Docker Model Runner:
```
docker model run hf.co/MatterWaveAI/grt-20m:F32
```

Lemonade

How to use MatterWaveAI/grt-20m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MatterWaveAI/grt-20m:F32

Run and chat with the model

lemonade run user.grt-20m-F32

List all available models

lemonade list

Atomic Chat

davidhouseholter commited on 6 days ago

Commit

b82a90b

verified ·

1 Parent(s): 6855923

Fix model card formatting (replace raw LaTeX with clean Unicode and code spans)

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -21,7 +21,7 @@ pipeline_tag: text-generation
 # GRT-20M: From NeurIPS Submission (`PairAdam`) to Symplectic/Huygens Optimization
-> **Note:** `GRT-20M` (`16.7M` total parameters, `10.3M` non-embedding backbone parameters across `12` rotational blocks) was pretrained across an 8-corpus English mix using first-generation `PairAdam` for our NeurIPS submission (archived pre-fine-tune at [`MatterWaveAI/grt-20m-base`](https://huggingface.co/MatterWaveAI/grt-20m-base)). Keeping the exact same `16.7M` NeurIPS submission checkpoint and swapping in our new classical geometric optimizers (`SymplecticAdam` and `HuygensSymplecticAdam`) lifts 9-task `SLM-Bench` multiple-choice accuracy from **`38.63%` (`PairAdam`) $\rightarrow$ `45.03%` (`HuygensSymplecticAdam`, `+6.40%`)** and cuts `GSM8K` held-out math perplexity from **`60.12` $\rightarrow$ `9.11` (`6.60×` reduction vs. Zero-Shot, `-27.2%` vs. `PairAdam`)**.
 A Geometric Rotation Transformer pretrained on a mix of eight English corpora,
 including FineWeb-Edu, Cosmopedia, and FineMath. GRT replaces the dense linear
@@ -103,8 +103,8 @@ Evaluated on the `SLM-Bench` small-language-model suite (`training/slm_bench.py`
 | Task (`SLM-Bench`) | Split (`n`) | `GRT-20M` Zero-Shot | NeurIPS Baseline (`PairAdam`) | `SymplecticAdam` | New Optimizer (`HuygensSymplecticAdam`) | Improvement |
 |---|---|---:|---:|---:|---:|---:|
-| **GSM8K Held-Out Perplexity (`PPL` $\downarrow$)** | `test` (`500`) | `60.12` | `12.52` | `10.70` | **`9.11`** | **`6.60×` lower vs. Zero-Shot (`-27.2%` vs. `PairAdam`)** |
-| **GSM8K 4-Way Numerical Verification (`Acc` $\uparrow$)** | `test` (`500`) | `21.00%` | `28.80%` | `91.40%` | **`93.20%`** | **`+64.40%` vs. `PairAdam`** |
 Reproduce with `grt-slm-bench`:

 # GRT-20M: From NeurIPS Submission (`PairAdam`) to Symplectic/Huygens Optimization
+> **Note:** `GRT-20M` (`16.7M` total parameters, `10.3M` non-embedding backbone parameters across `12` rotational blocks) was pretrained across an 8-corpus English mix using first-generation `PairAdam` for our NeurIPS submission (archived pre-fine-tune at [`MatterWaveAI/grt-20m-base`](https://huggingface.co/MatterWaveAI/grt-20m-base)). Keeping the exact same `16.7M` NeurIPS submission checkpoint and swapping in our new classical geometric optimizers (`SymplecticAdam` and `HuygensSymplecticAdam`) lifts 9-task `SLM-Bench` multiple-choice accuracy from **`38.63%` (`PairAdam`) → `45.03%` (`HuygensSymplecticAdam`, `+6.40%`)** and cuts `GSM8K` held-out math perplexity from **`60.12` → `9.11` (`6.60×` reduction vs. Zero-Shot, `-27.2%` vs. `PairAdam`)**.
 A Geometric Rotation Transformer pretrained on a mix of eight English corpora,
 including FineWeb-Edu, Cosmopedia, and FineMath. GRT replaces the dense linear
 | Task (`SLM-Bench`) | Split (`n`) | `GRT-20M` Zero-Shot | NeurIPS Baseline (`PairAdam`) | `SymplecticAdam` | New Optimizer (`HuygensSymplecticAdam`) | Improvement |
 |---|---|---:|---:|---:|---:|---:|
+| **GSM8K Held-Out Perplexity (`PPL` ↓)** | `test` (`500`) | `60.12` | `12.52` | `10.70` | **`9.11`** | **`6.60×` lower vs. Zero-Shot (`-27.2%` vs. `PairAdam`)** |
+| **GSM8K 4-Way Numerical Verification (`Acc` ↑)** | `test` (`500`) | `21.00%` | `28.80%` | `91.40%` | **`93.20%`** | **`+64.40%` vs. `PairAdam`** |
 Reproduce with `grt-slm-bench`: