Instructions to use mossez-systems/Mossez-100M-Coder-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Mossez-100M-Coder-Base

File size: 663 Bytes

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# Evaluation

The selected model was evaluated against its published Base source using frozen
packed validation/test data plus completion and FIM slices.

| Metric | Published Base loss | Coder-Base loss | Relative change |
|---|---:|---:|---:|
| Packed validation | 2.572834 | 1.488147 | -42.16% |
| Packed test | 2.604606 | 1.798295 | -30.96% |
| Completion validation | 2.286497 | 1.521212 | -33.47% |
| FIM validation | 2.408383 | 1.394937 | -42.08% |

All four frozen loss metrics improved. These tests measure a narrow local code
corpus and do not establish correctness on HumanEval, MBPP, repository-level
work, security tasks, or long-context generation.