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

Libraries

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

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

GigaKriya-ablation-NonEDU-1.5B / emissions.csv

shiza-fatimah

Upload emissions.csv with huggingface_hub

ed94a93 verified 6 months ago

Raw History Blame Contribute Delete

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	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
	2026-02-20T09:24:59,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,26785.31292648008,5.868297276903602,0.0002190863811451,45.02002227428573,483.3290648030823,70.0,0.3234439988384853,14.578001712114258,0.5029309182731369,15.404376629225888,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
	2026-02-20T09:26:05,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,26851.70894158515,5.881309177043166,0.0002190292316156,45.01771230375001,753.9321128972492,70.0,0.3242421290899864,14.61011894003024,0.5041720114498275,15.438533080570062,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
	2026-02-20T16:51:35,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,53581.48082679906,11.752862903111623,0.0002193456157194,45.02191580181819,582.5292195043088,70.0,0.6470075594240456,29.19840200064734,1.0060483822088095,30.851457942280152,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
	2026-02-21T00:41:32,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,81778.28355850605,17.71606298414066,0.0002166352999016,45.018362244375005,470.7500674388388,70.0,0.987434494779746,43.98217044265264,1.5353510519116031,46.50495598934417,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
	2026-02-21T08:08:08,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,108574.43269317017,23.5915596361367,0.0002172846686918,45.020036210625,471.0886712156265,70.0,1.3109917277876435,58.57878112104295,2.038458247472575,61.92823109630319,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
	2026-02-21T15:33:45,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,135312.03725891095,29.463603827235502,0.0002177456228144,45.0201719507143,997.7286680271444,70.0,1.6338570386277944,73.16809754665368,2.5404888514836235,77.34244343676467,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
	2026-02-21T22:59:01,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,162027.71856361814,35.33422447188061,0.0002180751835866,45.01732789607144,883.0352571005352,70.0,1.9564513716644525,87.75436787538281,3.0420996973832626,92.75291894443,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
	2026-02-22T07:01:58,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,191004.75025823712,41.34332997382416,0.0002164518417365,45.0207571275,468.5204323890168,70.0,2.306004353246632,102.63532841041952,3.5855867108694,108.52691947453512,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
	2026-02-22T14:27:43,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,217749.32103981404,47.21570215112457,0.0002168351291551,45.01806705,464.71630512700216,70.0,2.628945502129847,117.22531100017405,4.087736281853019,123.9419927841569,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
	2026-02-22T14:27:58,Polyglot,4dec4e64-d38a-49d7-a7e6-8bfd736432c0,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,217764.32712151902,47.218713069611404,0.0002168340136043584,45.0331776,1731.3896699155935,70.0,2.6291201831241495,117.23276840502884,4.08800790643004,123.94989649458302,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