Instructions to use Yemen-JPT/Micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yemen-JPT/Micro with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yemen-JPT/Micro")
model = AutoModelForCausalLM.from_pretrained("Yemen-JPT/Micro")
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 Settings

vLLM

How to use Yemen-JPT/Micro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Yemen-JPT/Micro

SGLang

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

Docker Model Runner
How to use Yemen-JPT/Micro with Docker Model Runner:
```
docker model run hf.co/Yemen-JPT/Micro
```

Micro

Commit History

Update README with bilingual YAML frontmatter and links

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Ameen commited on May 16

Add bilingual YAML frontmatter and metadata

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Ubuntu commited on May 16

Update README with Arabic documentation

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Ameen commited on May 16

Upload OSINT-Micro - Lightweight ONNX model for microservices (5.7GB)

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Ameen commited on May 16

initial commit

3b7d988
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Ameen commited on May 16

Commit History

Update README with bilingual YAML frontmatter and links 1d5ee65 verified

Add bilingual YAML frontmatter and metadata 2a7681d

Update README with Arabic documentation f9f7bd8 verified

Upload OSINT-Micro - Lightweight ONNX model for microservices (5.7GB) 706f886 verified

initial commit 3b7d988 verified

Update README with bilingual YAML frontmatter and links

1d5ee65
verified

Add bilingual YAML frontmatter and metadata

2a7681d

Update README with Arabic documentation

f9f7bd8
verified

Upload OSINT-Micro - Lightweight ONNX model for microservices (5.7GB)

706f886
verified

initial commit

3b7d988
verified