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 12

Commit

8bd2e83

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1 Parent(s): f09a2f5

Update README.md

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@@ -6,33 +6,58 @@ tags:
 - base_model:adapter:microsoft/Phi-3-mini-4k-instruct
 - lora
 - transformers
 ---
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 ### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]

 - base_model:adapter:microsoft/Phi-3-mini-4k-instruct
 - lora
 - transformers
+license: apache-2.0
+datasets:
+- teknium/OpenHermes-2.5
+- Magpie-Align/Magpie-Phi3-Pro-300K-Filtered
+language:
+- en
 ---
 # 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.
 ## Model Details
+OpenHermes dataset:
+1 Epoch
+8 Batch Size
+1 Gradient Accumulation
+5e-5 LR
+16 LoRa r
+32 LoRa Alpha
+300 Warmup steps
+500 Eval steps
+Trained only on Attention layers.
+Magpie  dataset:
+1 Epoch
+16 Batch Size
+1 Gradient Accumulation
+1e-4 LR
+16 LoRa r
+32 LoRa Alpha
+150 Warmup steps
+500 Eval steps
+Trained with Gate, Up, and Down layers.
 ### Model Description
+This model excels at creating bullet point formatting, while still mantaining
+- **Developed by:** Turtle170 (anonymous
+- **Language(s) (NLP):** English
+- **License:** apache-2.0
+- **Finetuned from model :** Phi-3-Mini-4k-Instruct with turtle170/Phi-3-Mini-OpenHermes-V1 adapters
 ### Model Sources [optional]
+<!-- Provide the basic
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]