Instructions to use luzimu/FullStack-Learn-LM-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luzimu/FullStack-Learn-LM-30B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="luzimu/FullStack-Learn-LM-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luzimu/FullStack-Learn-LM-30B-A3B")
model = AutoModelForCausalLM.from_pretrained("luzimu/FullStack-Learn-LM-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use luzimu/FullStack-Learn-LM-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luzimu/FullStack-Learn-LM-30B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luzimu/FullStack-Learn-LM-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luzimu/FullStack-Learn-LM-30B-A3B

SGLang

How to use luzimu/FullStack-Learn-LM-30B-A3B 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 "luzimu/FullStack-Learn-LM-30B-A3B" \
    --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": "luzimu/FullStack-Learn-LM-30B-A3B",
		"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 "luzimu/FullStack-Learn-LM-30B-A3B" \
        --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": "luzimu/FullStack-Learn-LM-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luzimu/FullStack-Learn-LM-30B-A3B with Docker Model Runner:
```
docker model run hf.co/luzimu/FullStack-Learn-LM-30B-A3B
```

Add library_name and citation, fix quick start links

by nielsr HF Staff - opened Feb 4

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+25

-6

nielsr

Feb 4

Hi! I'm Niels from the Hugging Face community science team.

I'm opening this pull request to improve the model card for FullStack-Learn-LM-30B-A3B:

Added library_name: transformers to the metadata. This is based on the config.json which shows compatibility with the Transformers library.
Corrected a duplicate link in the "Quick Start" section to properly point to the FullStack-Bench repository.
Added a "Citation" section at the bottom using the BibTeX provided in your GitHub repository.

These changes will help users discover, use, and cite your work more effectively. Let me know if you have any questions!

Add library_name and citation, fix quick start links79e528cb

luzimu changed pull request status to merged Feb 6

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