Instructions to use turtle170/MicroAtlas-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turtle170/MicroAtlas-V1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
model = PeftModel.from_pretrained(base_model, "turtle170/MicroAtlas-V1")

Transformers

How to use turtle170/MicroAtlas-V1 with Transformers:

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

pipe = pipeline("text-generation", model="turtle170/MicroAtlas-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("turtle170/MicroAtlas-V1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use turtle170/MicroAtlas-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turtle170/MicroAtlas-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turtle170/MicroAtlas-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/turtle170/MicroAtlas-V1

SGLang

How to use turtle170/MicroAtlas-V1 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 "turtle170/MicroAtlas-V1" \
    --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": "turtle170/MicroAtlas-V1",
		"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 "turtle170/MicroAtlas-V1" \
        --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": "turtle170/MicroAtlas-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use turtle170/MicroAtlas-V1 with Docker Model Runner:
```
docker model run hf.co/turtle170/MicroAtlas-V1
```

turtle170 commited on Jan 14

Commit

105a42f

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1 Parent(s): 15bdfbc

Update README.md

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@@ -16,9 +16,11 @@ language:
 # Model Card for Model ID
-Phi-3-Mini-OpenHermes-Magpie-V1 is a general purpose model trained on both the teknium/OpenHermes-2.5 dataset and the Magpie-Align/Phi3-Pro-300K-Filtered dataset
 and designed to provide speed, efficiency, and intelligence while still being relatively small.
@@ -78,7 +80,7 @@ This model excels at creating bullet point formatting.
 ### Direct Use
-For direct use, the easiest method is to just download the .gguf file from  and loading it into llama.cpp or Ollama.
 ### Out-of-Scope Use

 # Model Card for Model ID
+MicroAtlas-V1 is a general purpose model trained on both the teknium/OpenHermes-2.5 dataset and the Magpie-Align/Phi3-Pro-300K-Filtered dataset
 and designed to provide speed, efficiency, and intelligence while still being relatively small.
+the .gguf version of this model has only 3.15 M parameters, making it extremely small.
 ### Direct Use
+For direct use, the easiest method is to just download the .gguf file from turtle170/Phi-3-Mini-OpenHermes-Magpie-V1-F16-GGUF and loading it into llama.cpp or Ollama.
 ### Out-of-Scope Use