Text Generation
Transformers
PyTorch
English
custom-architecture
rope
rmsnorm
swiglu
flash-attention
16k-context
Eval Results (legacy)
Instructions to use Austin207/Map-NEO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Austin207/Map-NEO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Austin207/Map-NEO")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Austin207/Map-NEO", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Austin207/Map-NEO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Austin207/Map-NEO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Austin207/Map-NEO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Austin207/Map-NEO
- SGLang
How to use Austin207/Map-NEO 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 "Austin207/Map-NEO" \ --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": "Austin207/Map-NEO", "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 "Austin207/Map-NEO" \ --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": "Austin207/Map-NEO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Austin207/Map-NEO with Docker Model Runner:
docker model run hf.co/Austin207/Map-NEO
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**MAP-NEO Mini** is a 253M parameter autoregressive language model built from scratch with modern architectural improvements. It demonstrates that high-quality language models can be trained efficiently on modest hardware while achieving competitive performance through careful data curation and architectural choices.
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- **Developed by**:
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- **Model type**: Autoregressive Language Model
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- **Language(s)**: English
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- **License**: MIT
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**MAP-NEO Mini** is a 253M parameter autoregressive language model built from scratch with modern architectural improvements. It demonstrates that high-quality language models can be trained efficiently on modest hardware while achieving competitive performance through careful data curation and architectural choices.
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- **Developed by**: Antony Austin
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- **Model type**: Autoregressive Language Model
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- **Language(s)**: English
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- **License**: MIT
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