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

Libraries

How to use Polygl0t/GigaKriya-ablation-EDU-1.5B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Polygl0t/GigaKriya-ablation-EDU-1.5B")
model = AutoModelForCausalLM.from_pretrained("Polygl0t/GigaKriya-ablation-EDU-1.5B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Polygl0t/GigaKriya-ablation-EDU-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Polygl0t/GigaKriya-ablation-EDU-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/GigaKriya-ablation-EDU-1.5B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Polygl0t/GigaKriya-ablation-EDU-1.5B

SGLang

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

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

GigaKriya-ablation-EDU-1.5B

File size: 5,124 Bytes

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2026-02-17T06:37:02,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,26617.678940754035,5.857562133718051,0.0002200628442004,45.025996545000005,493.4659594236467,70.0,0.3214330571601183,14.554951101173778,0.4998125439053784,15.376196702239271,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-17T06:38:09,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,26684.170777371968,5.87065491010304,0.0002200051468371,45.019842907500006,747.771814295648,70.0,0.3222321673607197,14.58727812064648,0.5010551670473742,15.410565455054574,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-17T14:01:58,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,53313.97032340604,11.733347462364772,0.0002200801664402,45.016755862500005,497.3567442833152,70.0,0.6438015977474,29.15534829287109,1.0010796995240294,30.800229590142465,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-17T21:26:18,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,79973.21776941698,17.60888226215409,0.0002201847412583,45.02025,483.761832118092,70.0,0.9657266362237034,43.75622223800533,1.5016559614557332,46.22360483568471,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-18T04:50:03,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,106598.70932848495,23.471415925392684,0.0002201848040492,45.021894624000005,485.902051741892,70.0,1.287250267308472,58.32399268526793,2.001608931903861,61.61285188448007,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-18T12:13:49,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,133224.49818046798,29.335773727947632,0.0002201980426168,45.02207395800001,490.3854667951326,70.0,1.6087776082665364,72.89654246329843,2.5015672573434053,77.00688732890833,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-18T19:37:35,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,159850.41779878805,35.19742417262002,0.0002201897540044,45.033307443000005,492.6348082723465,70.0,1.93030093368484,87.46199559731826,3.001519383474029,92.393815914477,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-19T03:01:15,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,186470.57201988687,41.06008331948611,0.0002201960495681,45.01763879625,839.0296724532506,70.0,2.251760831848434,102.03025883636398,3.5013726916412446,107.78339235985328,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-19T10:25:04,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,213099.0852340669,46.92433908689937,0.0002201996270202,45.01379875500001,487.2486471710642,70.0,2.5733106433909683,116.6024841024682,4.00136521450208,123.1771599603606,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0
2026-02-19T10:25:17,Polyglot,d950d396-03ef-4c98-bbed-38448f7430f7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,213112.91568769608,46.927386210197774,0.00022019963482160313,0.0,513.7478079534567,70.0,2.5734717012205683,116.60981238388638,4.001874623904796,123.18515870901108,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-570.35.1.el9_6.x86_64-x86_64-with-glibc2.34,3.12.3,3.0.6,256,AMD EPYC 7713 64-Core Processor,4,4 x NVIDIA A100-SXM4-80GB,7.0932,50.7263,1950,machine,N,1.0