Instructions to use Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B")
model = AutoModelForCausalLM.from_pretrained("Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B

SGLang

How to use Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B 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 "Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B" \
    --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": "Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B",
		"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 "Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B" \
        --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": "Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B with Docker Model Runner:
```
docker model run hf.co/Polygl0t/GigaVerbo-v2-ablation-EDU-Synth-1.5B
```

nicholasKluge commited on Dec 27, 2025

Commit

1a8dae5

verified ·

1 Parent(s): e6e1b24

Update training_config.yaml

Browse files

Files changed (1) hide show

training_config.yaml +1 -1

training_config.yaml CHANGED Viewed

@@ -47,7 +47,7 @@ beta1: 0.9
 beta2: 0.95
 eps: 0.00000001
 lr_decay_type: "cosine"
-use_neg_sqrt: false
 lr_decay_iters_coef: 1.0
 seed: 1337
 max_steps: 22000

 beta2: 0.95
 eps: 0.00000001
 lr_decay_type: "cosine"
+use_sqrt: false
 lr_decay_iters_coef: 1.0
 seed: 1337
 max_steps: 22000