Instructions to use nazdef/1gpu-llm-small-en-it-base-v2 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-v2 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-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nazdef/1gpu-llm-small-en-it-base-v2")
model = AutoModelForCausalLM.from_pretrained("nazdef/1gpu-llm-small-en-it-base-v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nazdef/1gpu-llm-small-en-it-base-v2 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-v2"
# 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-v2",
		"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-v2

SGLang

How to use nazdef/1gpu-llm-small-en-it-base-v2 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-v2" \
    --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-v2",
		"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-v2" \
        --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-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nazdef/1gpu-llm-small-en-it-base-v2 with Docker Model Runner:
```
docker model run hf.co/nazdef/1gpu-llm-small-en-it-base-v2
```

1gpu-llm-small-en-it-base-v2 / report_small_v2_decoding_grid.md

nazdef

Publish 1gpu-llm-small-en-it-base-v2 from step_18800

3ce48eb verified 12 days ago

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GPT2-small decoding-grid comparison: step_8600 vs step_18000 vs step_18800

Date: 2026-07-03

Scope

This note compares the completed GPU decoding-grid artifacts for:

step_8600
step_18000
step_18800

The comparison focuses on:

Italian behavior
loop / repetition resistance
cloze / factual prompts
free EN / IT prompts

The tuning split is the main grid-selection signal. The holdout split is a confirmation check.

Executive reading

Checkpoint-level verdict: step_18800 remains the benchmark / loss winner.
Preset-level verdict: step_18000 + anti_loop is the strongest overall decoding-grid pairing once tuning and holdout are both considered.
Why that split matters: the raw tuning winner is step_18800 + anti_loop, but the raw holdout winner is step_18000 + anti_loop, and the average of the two split scores favors step_18000 + anti_loop.

Score table

Checkpoint	Tuning winner	Tuning score	Holdout winner	Holdout score	Tuning+holdout avg	Read
`step_8600`	`balanced`	2.789	`balanced`	3.188	2.989	stable, but weakest overall
`step_18000`	`creative`	3.370	`anti_loop`	3.927	3.496	best combined grid result
`step_18800`	`anti_loop`	3.034	`anti_loop`	3.210	3.122	consistent, but below `step_18000` on the combined read

Category notes

Italian behavior

step_8600 is the weakest on Italian factual prompts, but it still occasionally lands the right answer under balanced.
step_18000 improves English factual completion materially and keeps Italian free-form output coherent, but it still misses the Italian cloze answer in the best zero-loop setting.
step_18800 is the most visibly polished in style, but it regresses on the Italian cloze prompt: it keeps drifting into the wrong factual chain instead of producing Roma.

Loop / repetition

greedy is unusable on all three checkpoints.
anti_loop is the only preset that is consistently safe on repetition across the sweep.
step_18000 + anti_loop is the best compromise: no loops on either split and the strongest combined score.
step_8600 is the most repetition-sensitive checkpoint overall.

Cloze / factual prompts

On the English cloze prompt, step_18000 is the strongest of the three checkpoints.
On the Italian cloze prompt, none of the checkpoints is fully reliable, but step_18800 is the most obviously wrong because it settles into the wrong entity chain rather than a near-miss.
The factual prompts are the clearest place where benchmark loss and decode quality diverge: step_18800 still wins on loss, but it does not produce the best factual decoding behavior.

Free EN / IT prompts

All three checkpoints mostly preserve language identity on the free-form prompts.
The main distinction is robustness:
- step_8600 is coherent but bland and more repetition-prone.
- step_18000 + anti_loop is the cleanest balanced setting for free EN / IT generation.
- step_18800 reads smoother, but its factual slips keep it from being the best overall operational choice.

Tuning / holdout mismatch

step_8600 is internally stable: tuning and holdout both prefer balanced.
step_18000 is the clearest mismatch: tuning prefers creative, holdout prefers anti_loop.
step_18800 is internally consistent on the best preset (anti_loop), but the checkpoint still underperforms step_18000 + anti_loop on the combined read.

Bottom line

Checkpoint winner: step_18800
Best decoding-grid pairing: step_18000 + anti_loop
Main tradeoff: step_18800 is the best loss checkpoint, but step_18000 + anti_loop is the safer decode choice for Italian + free-form usage in this sweep.