Instructions to use mobilint/Llama-3.2-1B-Instruct-Batch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mobilint/Llama-3.2-1B-Instruct-Batch32 with Transformers:

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

pipe = pipeline("text-generation", model="mobilint/Llama-3.2-1B-Instruct-Batch32", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mobilint/Llama-3.2-1B-Instruct-Batch32", trust_remote_code=True, dtype="auto", device_map="auto")

Mobilint

How to use mobilint/Llama-3.2-1B-Instruct-Batch32 with Mobilint:

# pip install mblt-model-zoo
from mblt_model_zoo.vision import MBLT_Engine

model = MBLT_Engine(
    model_cls="Llama-3.2-1B-Instruct-Batch32",
    model_type="DEFAULT",
    model_path="",
    core_mode="global8",
)

try:
    image = model.preprocess("path/to/image.jpg")
    output = model(image)
    result = model.postprocess(output)
finally:
    model.dispose()

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mobilint/Llama-3.2-1B-Instruct-Batch32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mobilint/Llama-3.2-1B-Instruct-Batch32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mobilint/Llama-3.2-1B-Instruct-Batch32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mobilint/Llama-3.2-1B-Instruct-Batch32

SGLang

How to use mobilint/Llama-3.2-1B-Instruct-Batch32 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 "mobilint/Llama-3.2-1B-Instruct-Batch32" \
    --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": "mobilint/Llama-3.2-1B-Instruct-Batch32",
		"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 "mobilint/Llama-3.2-1B-Instruct-Batch32" \
        --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": "mobilint/Llama-3.2-1B-Instruct-Batch32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mobilint/Llama-3.2-1B-Instruct-Batch32 with Docker Model Runner:
```
docker model run hf.co/mobilint/Llama-3.2-1B-Instruct-Batch32
```

Llama-3.2-1B-Instruct-Batch32 / Llama-3.2-1B-Instruct-Batch32-W4V8.mxq

lbs1163

feat: add batch32 w4v8 model assets

f60d241 verified 3 months ago

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