Instructions to use nazdef/1gpu-llm-small-en-it-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nazdef/1gpu-llm-small-en-it-base with Transformers:

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

pipe = pipeline("text-generation", model="nazdef/1gpu-llm-small-en-it-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nazdef/1gpu-llm-small-en-it-base")
model = AutoModelForCausalLM.from_pretrained("nazdef/1gpu-llm-small-en-it-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nazdef/1gpu-llm-small-en-it-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nazdef/1gpu-llm-small-en-it-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nazdef/1gpu-llm-small-en-it-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nazdef/1gpu-llm-small-en-it-base

SGLang

How to use nazdef/1gpu-llm-small-en-it-base 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 "nazdef/1gpu-llm-small-en-it-base" \
    --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": "nazdef/1gpu-llm-small-en-it-base",
		"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 "nazdef/1gpu-llm-small-en-it-base" \
        --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": "nazdef/1gpu-llm-small-en-it-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nazdef/1gpu-llm-small-en-it-base with Docker Model Runner:
```
docker model run hf.co/nazdef/1gpu-llm-small-en-it-base
```

1gpu-llm-small-en-it-base / eval_summary.json

nazdef

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	{
	"comparison_path": "/mnt/apps/llm-nanochat/evals/20260623_0217_gpt2small_preln_step8000tail_cpu_full_benchmark/comparison.json",
	"metadata_path": "/mnt/apps/llm-nanochat/evals/20260623_0217_gpt2small_preln_step8000tail_cpu_full_benchmark/eval_metadata.json",
	"num_checkpoints": 16,
	"out_dir": "/mnt/apps/llm-nanochat/evals/20260623_0217_gpt2small_preln_step8000tail_cpu_full_benchmark",
	"recommended_checkpoint": {
	"checkpoint_name": "step_8600",
	"checkpoint_path": "/mnt/apps/llm-nanochat/checkpoints/20260622_resume-gpt2small-gpt2preln-k20-wsds800-final2e5-webwiki-step8000-dense50/step_8600.pt",
	"direction": "min",
	"value": 4.796363634940906
	},
	"report_path": "/mnt/apps/llm-nanochat/evals/20260623_0217_gpt2small_preln_step8000tail_cpu_full_benchmark/report.md",
	"suite": "pretrain_minimal_en_it_webwiki_step11000"
	}