Instructions to use learning-unit/L1-30B-A5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use learning-unit/L1-30B-A5B with Transformers:

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

pipe = pipeline("text-generation", model="learning-unit/L1-30B-A5B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("learning-unit/L1-30B-A5B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use learning-unit/L1-30B-A5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "learning-unit/L1-30B-A5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "learning-unit/L1-30B-A5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/learning-unit/L1-30B-A5B

SGLang

How to use learning-unit/L1-30B-A5B 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 "learning-unit/L1-30B-A5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "learning-unit/L1-30B-A5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "learning-unit/L1-30B-A5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "learning-unit/L1-30B-A5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use learning-unit/L1-30B-A5B with Docker Model Runner:
```
docker model run hf.co/learning-unit/L1-30B-A5B
```

L1-30B-A5B / generation_config.json

jeffkang-lunit

Add files using upload-large-folder tool

d7a755c verified about 2 months ago

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193 Bytes

{
  "_from_model_config": true,
  "do_sample": true,
  "repetition_penalty": 1.05,
  "eos_token_id": [
    151336,
    151338
  ],
  "pad_token_id": 151329,
  "transformers_version": "5.8.1"
}

	{
	"_from_model_config": true,
	"do_sample": true,
	"repetition_penalty": 1.05,
	"eos_token_id": [
	151336,
	151338
	],
	"pad_token_id": 151329,
	"transformers_version": "5.8.1"
	}