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

File size: 1,113 Bytes

7b5acd6

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