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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nazdef/1gpu-llm-medium-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-medium-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-medium-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-medium-en-it-base-v2

SGLang

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

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

1gpu-llm-medium-en-it-base-v2 / recommended_decoding_params.json

nazdef

docs: set official balanced generation defaults

019c0e3 verified about 7 hours ago

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	{
	"checkpoint": "step_34200",
	"preset": "balanced",
	"max_new_tokens": 64,
	"temperature": 0.3,
	"top_k": 50,
	"top_p": 0.95,
	"repetition_penalty": 1.1,
	"no_repeat_ngram_size": 0,
	"eos_token_id": 2,
	"pad_token_id": 0,
	"evidence": {
	"tuning_score": 3.1683,
	"holdout_score": 3.8405,
	"eos_rate_tuning": 1.0,
	"eos_rate_holdout": 1.0,
	"strict_loop_rate_tuning": 0.0,
	"strict_loop_rate_holdout": 0.0
	}
	}