Instructions to use mossez-systems/Mossez-100M-Coder-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mossez-systems/Mossez-100M-Coder-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mossez-systems/Mossez-100M-Coder-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mossez-systems/Mossez-100M-Coder-Base") model = AutoModelForCausalLM.from_pretrained("mossez-systems/Mossez-100M-Coder-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mossez-systems/Mossez-100M-Coder-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mossez-systems/Mossez-100M-Coder-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mossez-systems/Mossez-100M-Coder-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mossez-systems/Mossez-100M-Coder-Base
- SGLang
How to use mossez-systems/Mossez-100M-Coder-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 "mossez-systems/Mossez-100M-Coder-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": "mossez-systems/Mossez-100M-Coder-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 "mossez-systems/Mossez-100M-Coder-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": "mossez-systems/Mossez-100M-Coder-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mossez-systems/Mossez-100M-Coder-Base with Docker Model Runner:
docker model run hf.co/mossez-systems/Mossez-100M-Coder-Base
Dataset attribution
Continued pretraining used pinned snapshots of 60 permissively licensed public repositories. Raw source repositories are not redistributed in this model repo. The local build retained repository, commit, path, SPDX license label, split, and content hash per record.
Safety processing removed secret-like material, one benchmark-leakage record, and exact/normalized/near duplicates across splits. Evaluation records won any cross-split duplicate conflict. The packed corpus contains 39,999,488 training, 2,178,048 validation, and 2,561,024 test tokens.
Frozen sources
All source licenses remain the responsibility of their respective copyright holders. The model release itself is provided under Apache-2.0.