Instructions to use MaagDeveloper/NeoLM-Small-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaagDeveloper/NeoLM-Small-Base with Transformers:

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

pipe = pipeline("text-generation", model="MaagDeveloper/NeoLM-Small-Base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MaagDeveloper/NeoLM-Small-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaagDeveloper/NeoLM-Small-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaagDeveloper/NeoLM-Small-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaagDeveloper/NeoLM-Small-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaagDeveloper/NeoLM-Small-Base

SGLang

How to use MaagDeveloper/NeoLM-Small-Base 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 "MaagDeveloper/NeoLM-Small-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaagDeveloper/NeoLM-Small-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MaagDeveloper/NeoLM-Small-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaagDeveloper/NeoLM-Small-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MaagDeveloper/NeoLM-Small-Base with Docker Model Runner:
```
docker model run hf.co/MaagDeveloper/NeoLM-Small-Base
```

MaagDeveloper commited on 8 days ago

Commit

9d2a08c

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1 Parent(s): 2cf5ae9

Update README.md

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@@ -17,7 +17,7 @@ metrics:
 library_name: transformers
 ---
-# NeoLM-Small-Base — Stage 1: Foundation
 **NeoLM-Small-Base** is an English-focused, decoder-only Transformer language model trained from scratch with PyTorch and Hugging Face Transformers. This repository publishes the **Stage 1 Foundation** base-model checkpoint in **bfloat16** format. It is a **base next-token prediction model**, not an instruction-tuned chat assistant.

 library_name: transformers
 ---
+# NeoLM-Small-Base (61M) — Stage 1: Foundation
 **NeoLM-Small-Base** is an English-focused, decoder-only Transformer language model trained from scratch with PyTorch and Hugging Face Transformers. This repository publishes the **Stage 1 Foundation** base-model checkpoint in **bfloat16** format. It is a **base next-token prediction model**, not an instruction-tuned chat assistant.