Instructions to use leveldevai/MBA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leveldevai/MBA-7B with Transformers:

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

pipe = pipeline("text-generation", model="leveldevai/MBA-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("leveldevai/MBA-7B")
model = AutoModelForCausalLM.from_pretrained("leveldevai/MBA-7B")
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 leveldevai/MBA-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leveldevai/MBA-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leveldevai/MBA-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leveldevai/MBA-7B

SGLang

How to use leveldevai/MBA-7B 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 "leveldevai/MBA-7B" \
    --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": "leveldevai/MBA-7B",
		"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 "leveldevai/MBA-7B" \
        --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": "leveldevai/MBA-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use leveldevai/MBA-7B with Docker Model Runner:
```
docker model run hf.co/leveldevai/MBA-7B
```

Librarian Bot: Add base_model metadata to model

by librarian-bot - opened Jan 22, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

librarian-bot

Jan 22, 2024

This pull request aims to enrich the metadata of your model by adding base_model metadata to YAML block of your model's README.md.

How did we find this information? We use the mergekit_config.yml config from mergekit.

Why add this? Enhancing your model's metadata in this way:

Boosts Discoverability - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
Highlights Impact - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at librarian-bots/base_model_explorer.

This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien.

Librarian Bot: Add base_model metadata to model4c3e7474

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

Ready to merge

This branch is ready to get merged automatically.

· Sign up or log in to comment