Instructions to use b3x0m/xomdich-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use b3x0m/xomdich-mobile with Transformers:

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

pipe = pipeline("text-generation", model="b3x0m/xomdich-mobile")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("b3x0m/xomdich-mobile", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use b3x0m/xomdich-mobile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "b3x0m/xomdich-mobile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "b3x0m/xomdich-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/b3x0m/xomdich-mobile

SGLang

How to use b3x0m/xomdich-mobile 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 "b3x0m/xomdich-mobile" \
    --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": "b3x0m/xomdich-mobile",
		"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 "b3x0m/xomdich-mobile" \
        --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": "b3x0m/xomdich-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use b3x0m/xomdich-mobile with Docker Model Runner:
```
docker model run hf.co/b3x0m/xomdich-mobile
```

b3x0m commited on Aug 12, 2024

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@@ -142,10 +142,6 @@ Use the code below to get started with the model.
 ## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]
 - **Cloud Provider:** [More Information Needed]

 ## Environmental Impact
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]
 - **Cloud Provider:** [More Information Needed]