Instructions to use nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100 with Transformers:

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

pipe = pipeline("text-generation", model="nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100")
model = AutoModelForCausalLM.from_pretrained("nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100

SGLang

How to use nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100 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/20260713_resume-gpt2medium-step22000-d1800-step23100" \
    --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/20260713_resume-gpt2medium-step22000-d1800-step23100",
		"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/20260713_resume-gpt2medium-step22000-d1800-step23100" \
        --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/20260713_resume-gpt2medium-step22000-d1800-step23100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100 with Docker Model Runner:
```
docker model run hf.co/nazdef/20260713_resume-gpt2medium-step22000-d1800-step23100
```

20260713_resume-gpt2medium-step22000-d1800-step23100 / training_config.yaml

nazdef

Publish step_23100 decay-only checkpoint candidate

de3af9d verified about 1 month ago

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	# WSD-decay-only continuation from the medium continual-pretraining checkpoint at step_22000.
	# Intention: branch from a checkpoint that was still inside the stable plateau of the parent CPT run,
	# then apply a shorter explicit decay-only cooldown for dense checkpoint comparison.

	resume_from: /mnt/apps/llm-nanochat/checkpoints/20260703_continual-pretraining-gpt2medium-gpt2preln-k20-step14700-lr5e5-w500-s18500-d2000-final1e5-webwiki/step_22000.pt
	dataset_dir: /mnt/apps/llm-nanochat/datasets/202605141153_fineweb50_wiki50_50en_50it_score100_2500context_5Btokens_tok_20260515_en50it50_webwiki_stratified_500M
	output_dir: /mnt/apps/llm-nanochat/artifacts/runs/20260713_resume-gpt2medium-gpt2preln-k20-wsddecayonly-cpt14700-step22000-lr5e5-final1e5-webwiki-d1800
	tokenizer_dir: /mnt/apps/llm-nanochat/tokenizers/tokenizer_20260515_en50it50_webwiki_stratified_500M
	seed: 1337

	model:
	architecture: gpt2
	block_type: gpt2_prelayernorm
	tie_word_embeddings: true
	vocab_size: 32000
	dim: 1024
	n_layers: 24
	n_heads: 16

	training:
	sequence_length: 2500
	max_steps: 23800
	batch_size: 2
	grad_accum_steps: 48

	learning_rate: 5.0e-05
	peak_lr: 5.0e-05
	lr_schedule: wsd-decay-only

	warmup_steps: 0
	stable_steps: 0
	decay_steps: 1800
	final_lr: 1.0e-05

	adamw_betas:
	- 0.9
	- 0.95
	adamw_eps: 1.0e-08
	weight_decay: 0.1
	clip_grad_norm: 1.0

	save_every_steps: 100
	checkpoint_dir: /mnt/apps/llm-nanochat/checkpoints/20260713_resume-gpt2medium-gpt2preln-k20-wsddecayonly-cpt14700-step22000-lr5e5-final1e5-webwiki-d1800
	precision: bf16

	evaluation:
	validation_every_steps: 100
	validation_max_batches: 128
	probe_every_steps: 1000
	probe_tokenizer_dir: /mnt/apps/llm-nanochat/tokenizers/tokenizer_20260515_en50it50_webwiki_stratified_500M
	probe_max_new_tokens: 32
	probe_prompts:
	en:
	- prompt: "The capital of Italy is"
	expected_next_text: " Rome"
	- prompt: "A small language model should"
	expected_next_text: " be"
	it:
	- prompt: "La capitale d'Italia è"
	expected_next_text: " Roma"
	- prompt: "Un piccolo modello linguistico dovrebbe"
	expected_next_text: " essere"