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 / README.md

mossez-systems

Publish Mossez-100M-Coder-Base

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model:
	- mossez-systems/Mossez-100M-Base
	tags:
	- causal-lm
	- code
	- fill-in-the-middle
	- llama
	- research
	- experimental
	---

	# Mossez-100M-Coder-Base

	Mossez-100M-Coder-Base is an experimental 100M-parameter code completion and
	fill-in-the-middle model continued-pretrained from
	[`mossez-systems/Mossez-100M-Base`](https://huggingface.co/mossez-systems/Mossez-100M-Base).
	It is a base model, not a chat or instruction-following assistant.

	## Model details

	\| Property \| Value \|
	\|---\|---:\|
	\| Parameters \| 100,098,048 \|
	\| Architecture \| Llama-compatible decoder-only Transformer \|
	\| Layers / hidden size \| 12 / 768 \|
	\| Query / KV heads \| 12 / 4 \|
	\| Context length \| 1,024 tokens \|
	\| Vocabulary \| 32,007 \|
	\| Weight format \| Safetensors, FP32 \|
	\| License \| Apache-2.0 \|

	The tokenizer extends the Mossez-100M-Base vocabulary with seven single-token chat/FIM markers.
	Existing token IDs were not changed. The FIM markers are `<\|fim_prefix\|>` (32004),
	`<\|fim_middle\|>` (32005), and `<\|fim_suffix\|>` (32006).

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "mossez-systems/Mossez-100M-Coder-Base"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

	prompt = "def fibonacci(n: int) -> list[int]:
	"
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	output = model.generate(**inputs, do_sample=False, max_new_tokens=96)
	print(tokenizer.decode(output[0], skip_special_tokens=True))
	```

	For fill-in-the-middle, render the prompt as
	`<\|fim_prefix\|>{prefix}<\|fim_suffix\|>{suffix}<\|fim_middle\|>`.

	## Training and evaluation

	The model consumed 39,997,440 tokens in 9,765 finite optimizer steps without
	corpus wraparound. Packed validation loss decreased monotonically from 2.572834
	to 1.488147. See [TRAINING_REPORT.md](TRAINING_REPORT.md),
	[EVALUATION.md](EVALUATION.md), and [DATASET_ATTRIBUTION.md](DATASET_ATTRIBUTION.md).

	The released `model.safetensors` SHA-256 is
	`aba529bf10ad9f3acb5294c8bc2b4c93d20d25c6cff3a235a8659503b9ac1837`.

	## Limitations

	This small research model is not production-ready. It can emit malformed or
	insecure code, wrong constants, hallucinated APIs, repetition, and early EOS.
	Its 1,024-token context is short, and the evaluation suite is narrow. Validate,
	test, and sandbox every output. Do not use generated code without review.