Instructions to use MBZUAI/MobiLlama-05B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MBZUAI/MobiLlama-05B with Transformers:

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

pipe = pipeline("text-generation", model="MBZUAI/MobiLlama-05B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MBZUAI/MobiLlama-05B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MBZUAI/MobiLlama-05B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MBZUAI/MobiLlama-05B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MBZUAI/MobiLlama-05B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MBZUAI/MobiLlama-05B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MBZUAI/MobiLlama-05B

SGLang

How to use MBZUAI/MobiLlama-05B 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 "MBZUAI/MobiLlama-05B" \
    --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": "MBZUAI/MobiLlama-05B",
		"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 "MBZUAI/MobiLlama-05B" \
        --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": "MBZUAI/MobiLlama-05B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MBZUAI/MobiLlama-05B with Docker Model Runner:
```
docker model run hf.co/MBZUAI/MobiLlama-05B
```

Most similar existing model architecture?

by cckm - opened Mar 1, 2024

Discussion

cckm

Mar 1, 2024

I would love to try this model on inference platforms like llama.cpp and MLC. However, these platforms require some custom code for model conversion, so it would be easiest if I could start from the conversion code of an existing model, and then adapt it for MobiLlama. Which model's conversion code would you recommend I start from, and what are the key changes I need to pay attention to?

These are the architectures currently converted by llama.cpp:
https://github.com/ggerganov/llama.cpp/blob/052051d8ae4639a1c3c61e7da3237bcc572469d4/convert-hf-to-gguf.py#L178

and by MLC:
https://github.com/mlc-ai/mlc-llm/tree/main/python/mlc_chat/model

cckm

Mar 1, 2024

•

edited Mar 1, 2024

Ah, I see it now. The biggest change comes from params savings from sharing the MLP across all layers.

cckm changed discussion status to closed Mar 1, 2024

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