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

Libraries

How to use mossez-systems/Mossez-100M-Coder-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="mossez-systems/Mossez-100M-Coder-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mossez-systems/Mossez-100M-Coder-Instruct")
model = AutoModelForCausalLM.from_pretrained("mossez-systems/Mossez-100M-Coder-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mossez-systems/Mossez-100M-Coder-Instruct 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-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Coder-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mossez-systems/Mossez-100M-Coder-Instruct

SGLang

How to use mossez-systems/Mossez-100M-Coder-Instruct 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-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Coder-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mossez-systems/Mossez-100M-Coder-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mossez-systems/Mossez-100M-Coder-Instruct with Docker Model Runner:
```
docker model run hf.co/mossez-systems/Mossez-100M-Coder-Instruct
```

Mossez-100M-Coder-Instruct / EVALUATION.md

mossez-systems

Publish Mossez-100M-Coder-Instruct

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	# Evaluation

	Evaluation used frozen, source-group-disjoint validation and test splits with
	assistant-only loss. Lower is better.

	\| Model \| Validation loss \| Test loss \|
	\|---\|---:\|---:\|
	\| Source Coder-Base \| 2.008803 \| 2.023115 \|
	\| One-epoch Coder-Instruct \| 0.029855 \| 0.032257 \|

	## Per-task loss

	\| Task \| Source val \| Candidate val \| Source test \| Candidate test \|
	\|---\|---:\|---:\|---:\|---:\|
	\| bug_fix \| 0.666634 \| 0.002418 \| 0.685901 \| 0.002504 \|
	\| completion \| 1.098412 \| 0.072219 \| 1.184431 \| 0.074582 \|
	\| explain \| 3.757432 \| 0.003248 \| 3.861036 \| 0.003465 \|
	\| fim_repair \| 2.926159 \| 0.171918 \| 2.934412 \| 0.188182 \|
	\| json_yaml_conversion \| 1.304223 \| 0.002704 \| 1.310993 \| 0.002706 \|
	\| refactor \| 1.088105 \| 0.002698 \| 1.089293 \| 0.002557 \|
	\| shell \| 2.545998 \| 0.001775 \| 2.548895 \| 0.001855 \|
	\| short_function \| 1.503674 \| 0.003699 \| 1.520041 \| 0.003951 \|
	\| sql \| 2.761549 \| 0.018859 \| 2.715693 \| 0.021215 \|
	\| traceback \| 3.734223 \| 0.002768 \| 3.769516 \| 0.002884 \|
	\| unit_test \| 1.751150 \| 0.075700 \| 1.721415 \| 0.083690 \|

	Overall and all 11 per-task losses improved against both the source and the
	100-step SFT pilot. A deterministic 11-prompt generation smoke passed all
	local syntax/format/safety validators; 9 outputs exactly matched references.

	These results come from a small, project-authored, template-heavy corpus.
	They do not establish performance on HumanEval, MBPP, SWE-bench, security
	tasks, repository-level work, or arbitrary real-world prompts.

	# Evaluation

	Evaluation used frozen, source-group-disjoint validation and test splits with
	assistant-only loss. Lower is better.

	\| Model \| Validation loss \| Test loss \|
	\|---\|---:\|---:\|
	\| Source Coder-Base \| 2.008803 \| 2.023115 \|
	\| One-epoch Coder-Instruct \| 0.029855 \| 0.032257 \|

	## Per-task loss

	\| Task \| Source val \| Candidate val \| Source test \| Candidate test \|
	\|---\|---:\|---:\|---:\|---:\|
	\| bug_fix \| 0.666634 \| 0.002418 \| 0.685901 \| 0.002504 \|
	\| completion \| 1.098412 \| 0.072219 \| 1.184431 \| 0.074582 \|
	\| explain \| 3.757432 \| 0.003248 \| 3.861036 \| 0.003465 \|
	\| fim_repair \| 2.926159 \| 0.171918 \| 2.934412 \| 0.188182 \|
	\| json_yaml_conversion \| 1.304223 \| 0.002704 \| 1.310993 \| 0.002706 \|
	\| refactor \| 1.088105 \| 0.002698 \| 1.089293 \| 0.002557 \|
	\| shell \| 2.545998 \| 0.001775 \| 2.548895 \| 0.001855 \|
	\| short_function \| 1.503674 \| 0.003699 \| 1.520041 \| 0.003951 \|
	\| sql \| 2.761549 \| 0.018859 \| 2.715693 \| 0.021215 \|
	\| traceback \| 3.734223 \| 0.002768 \| 3.769516 \| 0.002884 \|
	\| unit_test \| 1.751150 \| 0.075700 \| 1.721415 \| 0.083690 \|

	Overall and all 11 per-task losses improved against both the source and the
	100-step SFT pilot. A deterministic 11-prompt generation smoke passed all
	local syntax/format/safety validators; 9 outputs exactly matched references.

	These results come from a small, project-authored, template-heavy corpus.
	They do not establish performance on HumanEval, MBPP, SWE-bench, security
	tasks, repository-level work, or arbitrary real-world prompts.