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 / decoding_report.md

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Decoding search report — `decoding_tuning_en_it_v1`

Scope

checkpoint: /mnt/apps/llm-nanochat/checkpoints/20260622_resume-gpt2small-gpt2preln-k20-wsds800-final2e5-webwiki-step8000-dense50/step_8600.pt
tokenizer: /mnt/apps/llm-nanochat/tokenizers/tokenizer_20260515_en50it50_webwiki_stratified_500M
tuning prompts: /tmp/llm-nanochat-dev-p0p1/eval_prompts/decoding_tuning.jsonl
holdout prompts: /tmp/llm-nanochat-dev-p0p1/eval_prompts/decoding_tuning_holdout.jsonl
seeds: [1337, 1338, 1339]
holdout top-k: 3

This report intentionally separates decoding tuning from the checkpoint-selection benchmark. The tuning prompt suite is the only data used to choose the recommended preset; holdout results are shown only as a confirmation check.

Tuning leaderboard

Rank	Preset	Score	Pass rate	Distinct-2	Loop rate	Repeated 4-gram	Lang. consistency	Length closeness	Avg tokens	Decoding
1	balanced	2.789	0.0833	0.9176	0	0.0952	0.9514	0.9487	48.5714	temp=0.8 top_k=50 top_p=0.95 rep=1.1 ng=0 max_new=64
2	creative	2.7751	0.0833	0.9565	0.0476	0.0952	0.9792	0.9286	54.8571	temp=1 top_k=100 top_p=0.95 rep=1.1 ng=0 max_new=64
3	anti_loop	2.7175	0	0.9261	0	0.0476	0.9583	0.9654	49.4286	temp=0.8 top_k=50 top_p=0.9 rep=1.15 ng=4 max_new=64
4	conservative	1.3666	0.25	0.6885	0.2857	0.6667	0.9353	0.9635	49.3333	temp=0.6 top_k=40 top_p=0.9 rep=1.05 ng=0 max_new=64
5	greedy	-0.9418	0	0.055	0.7143	0.7143	0.7708	0.971	49.7143	temp=0 top_k=0 top_p=1 rep=1 ng=0 max_new=64

Holdout confirmation

Holdout prompts are only used after tuning has already chosen candidates. They are not part of the tuning leaderboard.

Rank	Preset	Score	Pass rate	Distinct-2	Repeated 4-gram	Lang. consistency	Length closeness	Avg tokens
1	anti_loop	3.3392	0.3333	0.9385	0.0833	1	0.8428	59.25
2	balanced	3.1885	0.3333	0.9244	0.1667	0.9762	0.8607	58.3333
3	creative	3.096	0.1111	0.9579	0	1	0.9486	53.8333

Recommendation

recommended preset: balanced
tuning score: 2.789
holdout checked: True
holdout results should be used as confirmation, not as the tuning objective.

Notes

Large grids should be entered only after a coarse pass has identified a sensible band.
This workflow is intended for checkpoint-specific decoding tuning, not for declaring the best checkpoint itself.