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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MBZUAI/MobiLlama-05B-Chat"
# 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-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

Ashmal commited on Feb 26, 2024

Commit

86ed4ca

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1 Parent(s): 2f5d68a

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@@ -73,17 +73,17 @@ python3 -m fastchat.serve.cli --model-path MBZUAI/MobiLlama-05B-Chat
 ## Evaluation
-| Evaluation Benchmark | MobiLlama-05B-Chat | MobiLlama-1.2B-Chat |
 | ----------- | ----------- | ----------- |
-| HellaSwag | 0.5042 | 0.6244 |
-| MMLU | 0.2677 | 0.2635 |
-| Arc Challenge | 0.2935 | 0.3558 |
-| TruthfulQA | 0.3997 | 0.3848 |
-| CrowsPairs | 0.5694 | 0.679 |
-| PIQA | 0.7078 | 0.7557 |
-| Race | 0.3320 | 0.3598 |
-| SIQA | 0.4165 | 0.4396 |
-| Winogrande | 0.5659 | 0.5966 |
 ## Intended Uses

 ## Evaluation
+| Evaluation Benchmark | MobiLlama-0.5B | MobiLlama-0.8B | MobiLlama-1.2B |
 | ----------- | ----------- | ----------- |
+| HellaSwag | 0.5252 | 0.5409 | 0.6299 |
+| MMLU | 0.2645 | 0.2692 | 0.2423 |
+| Arc Challenge | 0.2952 | 0.3020 | 0.3455 |
+| TruthfulQA | 0.3805 | 0.3848 | 0.3557 |
+| CrowsPairs | 0.6403 | 0.6482 | 0.6812 |
+| PIQA | 0.7203 | 0.7317 | 0.7529 |
+| Race | 0.3368 | 0.3337 | 0.3531 |
+| SIQA | 0.4022 | 0.4160 | 0.4196 |
+| Winogrande | 0.5753 | 0.5745 | 0.6108 |
 ## Intended Uses