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
```

GigaVerbo-v2-ablation-EDU-Synth-1.5B / emissions.csv

nicholasKluge

Upload emissions.csv with huggingface_hub

898b039 verified 6 months ago

raw

history blame contribute delete

6.38 kB

	timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
	2025-11-22T06:25:09,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,31613.25393096404,8.244000432349367,0.0002607767125254,45.149149674,990.422086979608,70.0,0.3818124452351561,20.666380868647025,0.592443075012357,21.64063638889452,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-22T06:27:39,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,31763.76656902908,8.282828623079931,0.0002607634269405,45.13924048500001,1142.4038799168777,70.0,0.3836236955007326,20.76368359121136,0.5952537398791597,21.74256102659124,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-22T15:14:54,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,63398.30358983506,16.532553451641128,0.0002607728048782,45.12735640125,795.3632607415248,70.0,0.7657098640601526,41.444393594377246,1.188120591020942,43.39822404945827,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-23T00:02:14,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,95038.66005746904,24.7832615000595,0.0002607703168907,45.12421380375,1446.7715163215078,70.0,1.147851850187794,62.12753554032199,1.781080650071727,65.05646804058144,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-23T08:49:30,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,126674.16578492313,33.03409715828668,0.0002607800647716,45.1401727275,1449.290254627998,70.0,1.5299399141526429,82.81114845053247,2.37395864486144,86.71504700954634,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-23T17:36:58,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,158322.43852378102,41.28775047568845,0.0002607826841265,45.140673834000005,973.4895098589632,70.0,1.912154550599929,103.50183486945724,2.9670329600150573,108.38102238007204,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-24T02:24:41,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,189985.21572616813,49.54274855845636,0.0002607715993536,45.139323105,787.5901252836825,70.0,2.294550483723609,124.19558981944812,3.560387478575368,130.05052778174658,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-24T11:12:23,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,221647.29003088103,57.79818881727106,0.0002607665034353,45.12356587384616,1702.119271961065,70.0,2.676947538738762,144.89050208953142,4.153744274529637,151.72119390279843,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-24T19:59:59,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,253303.509333228,66.05204632445293,0.0002607624604109,45.118873305,777.4485655161078,70.0,3.059284840430322,165.58141245030248,4.747007984116654,173.3877052748469,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-25T04:47:42,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,284966.4849852461,74.30596612128329,0.0002607533518376,45.10885245750001,805.4063298716145,70.0,3.44169365517518,186.27230425939203,5.34038224367986,195.0543801582446,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-25T13:44:10,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,317154.8521652941,82.60549506125449,0.0002604579261432,45.10126903500001,798.3456038798357,70.0,3.8304807404871872,207.0666529637467,5.943646086435737,216.84077979066672,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-25T22:33:40,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,348924.35933438595,90.86642378495516,0.0002604186877588,45.115749529615385,1753.844439453577,70.0,4.214187083996457,227.77263671102057,6.539029427076008,238.5258532220899,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0
	2025-11-25T22:36:25,Polyglot,1ac22d0b-b416-4d5f-9003-a43345d296ac,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,349089.98651392804,90.90932306568932,0.0002604180199309,0.0,1852.123454506777,70.0,4.21618062071518,227.87987977209275,6.542404145033048,238.63846453783785,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.4,256,AMD EPYC 7713 64-Core Processor,8,8 x NVIDIA A40,7.1178,50.7246,975,machine,N,1.0