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 / decoding_grid_config.yaml

nazdef

Release 1gpu-llm-medium-v2 step 34200

7b5acd6 verified about 11 hours ago

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	name: medium_checkpoint_decoding_grid_1000
	checkpoint_path: null
	tokenizer_dir: null
	tuning_prompts_path: eval_prompts/decoding_tuning.jsonl
	holdout_prompts_path: eval_prompts/decoding_tuning_holdout.jsonl
	seeds:
	- 1337
	holdout_top_k: 4
	target_length_ratio: 1.0
	ranking_weights:
	prompt_pass_rate: 2.0
	completion_rate: 0.5
	distinct_2: 1.2
	language_consistency: 0.75
	length_closeness: 1.0
	loop_rate: -2.0
	repeated_4gram_rate: -1.5
	language_switch_rate: -0.75
	decoding_presets:
	- name: anti_loop_conservative
	max_new_tokens: 1000
	temperature: 0.3
	top_k: 50
	top_p: 0.9
	repetition_penalty: 1.15
	no_repeat_ngram_size: 4
	- name: anti_loop
	max_new_tokens: 1000
	temperature: 0.8
	top_k: 50
	top_p: 0.9
	repetition_penalty: 1.15
	no_repeat_ngram_size: 4
	- name: balanced
	max_new_tokens: 1000
	temperature: 0.8
	top_k: 50
	top_p: 0.95
	repetition_penalty: 1.1
	no_repeat_ngram_size: 0
	- name: creative
	max_new_tokens: 1000
	temperature: 1.0
	top_k: 100
	top_p: 0.95
	repetition_penalty: 1.1
	no_repeat_ngram_size: 0