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 / recommended_decoding_params.json

nazdef

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

3ce48eb verified 11 days ago

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	{
	"preset_name": "anti_loop",
	"selection_basis": "safety-first public default for the promoted 1gpu-llm-small-en-it-base-v2 release after the comparative GPU decoding sweep across step_8600, step_18000, and step_18800; step_18800 is the checkpoint-level winner, while step_18000 + anti_loop was the strongest overall pairing in the sweep",
	"checkpoint_level_winner": "step_18800",
	"preset_level_winner_of_sweep": "step_18000 + anti_loop",
	"alternate_preset_worth_trying_on_step_18800": "creative",
	"do_sample": true,
	"temperature": 0.8,
	"top_k": 50,
	"top_p": 0.9,
	"repetition_penalty": 1.15,
	"no_repeat_ngram_size": 4,
	"max_new_tokens": 64
	}